This PDF file contains the front matter associated with SPIE Proceedings Volume 7285, including the Title Page, Copyright information, Table of Contents, Introduction, and the Conference Committee listing.

Wavelet transform is an important pretreatment tool to extract texture information, which can decompose image as different level and provide rich detail information. But the tool is fit to any size spatial resolution image? The paper gives several tests and extracts texture information from high and medium spatial resolution images, compares the texture information; what's more, image classification is tested too. The result shows that wavelet transform cannot provide effective texture information for medium spatial resolution image.

This paper compares the normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) as indicators of surface urban heat island (SUHI) effects in MODIS imagery by investigating the relationships between the land surface temperature (LST), NDBI and the NDVI. MODIS data were used to estimate the LST, NDBI and NDVI from four different seasons for Changsha-Zhuzhou-Xiangtan metropolitan area. Our analysis indicates than there is a strong linear relationship between LST and NDBI, whereas the relationship between LST and NDVI is much less strong and varies by season. This result suggests NDBI is an accurate indicator of SUHI effects and can be used as a complementary metric to the traditionally applied NDVI for analyzing LST quantitatively over the seasons for SUHI studies.

In the preprocessing course of spatial data, different departments always have diverse naming methods when describing the same geographical entity, due to different backgrounds and views of angle. There is also great difference among the feature sets which are used to describe concepts of geo-ontology, making it difficult to conduct semantic interoperation based on the theory of concepts reasoning in the information science. Consequently, this paper takes green land system for example and presents a reasoning method of geo-ontology based on object-oriented remote sensing analysis. We firstly establish an image hierarchical network system by using the object-oriented multi-scale segmentation technology. Then, the mapping from domain ontologies to image objects is realized by the maximum area method. Finally, through analyzing the features of image objects, the reasoning principles are built up, realizing the semantic interoperation between concepts of ontologies and image objects.

The integration of the earth observation data and GIS data is a focal point in the research of remote sensing data analysis. In this paper, after the conflation of the high resolution images and the land-use maps the segment and their features are obtained. Then we do some researches about the uniformity between the features and land-use class. After the analyzing using the traditional method, the result shows that the features of the segment are not in accord with land-use class of it. Then we analyze this phenomenon and find three reasons. Moreover, the third reason, we think, may be the main factor. So we propose a approach establishing the mapping between the features and the land-use class. The result of the experiment is much better than the traditional method. It demonstrates the validity to deal with the non-uniformity in earth observation data and GIS data.

For information lost phenomenon on RS image, we present a robust completion algorithm based on a single image instead of adopting image placement measure. In order to avoid the occurrence of visually inconsistent results caused by greedy patch-by-patch manner, we creatively introduce GIS accessorial data to guide structure completion and pose this task in the form of a discrete global optimization problem. In the process of implementing this optimization scheme, our method integrates exemplar-based texture synthesis techniques and Dynamic programming algorithm. By this way, Information Compensation is separated into two independent processes to deal with. Comparative Experiments is conducted and prove the efficiency of our method successfully.

The aim of this study is to examine the feasibility of Support vector regression (SVR) in retrieval of suspended sediment concentration by comparing it with band ratio regression models. First, the remote sensing reflectance and the suspended sediment concentrations were measured in field and in laboratory. The in situ dataset and laboratory dataset were used in t developing retrieval models based on support vector regression and band ratio regression. Second, we select band ratio regression model with high R-square value and low Root Mean Squared Error as the best band ratio regression model. Finally, the best band ratio regression model was compared with SVR model in different datasets by leave-one-out cross validation. The experimental results demonstrate that the prediction accuracy of support vector regression outperforms the band ratio regression models based on the mean absolute error in general. SVR using all bands yielded slightly superior results than using TM1 and TM4 bands in terms of accuracy. The findings suggest that the SVR model is available using all bands data. The support vector regression can be applied in retrieval of suspended sediment concentration without selecting bands and constructing band ratio expression. SVR is a promising alternative to suspended sediment retrieval models.

Lizards are an "excellent group of organisms" to examine the habitat and microhabitat use mainly because their ecology and physiology is well studied. Due to their behavioral body temperature regulation, the thermal environment is especially linked with their habitat use. In this study, for mapping and understanding lizard's distribution at microhabitat scale, an individual of Timon Lepidus was kept and monitored in a terrarium (245×120×115cm) in which sand, rocks, burrows, hatching chambers, UV-lamps, fog generators and heating devices were placed to simulate its natural habitat. Optical cameras, thermal cameras and other data loggers were fixed and recording the lizard's body temperature, ground surface temperature, air temperature, radiation and other important environmental parameters. By analysis the data collected, we propose a Cellular Automata (CA) model by which the movement of lizards is simulated and translated into their distribution. This paper explores the capabilities of applying GIS techniques to thermoregulatory activity studies in a microhabitat-scale. We conclude that microhabitat use of lizards can be explained in some degree by the rule based CA model.

In this study, we monitored the quality of fresh tea leaves as raw materials of tea products by hyperspectral technology, as a way to explore the potential of hyperspectral remote sensing to detect the taste-related chemical components with low concentration in living plants. At leaf scale, empirical models have been established to find the relationships between quality-related chemicals in fresh tea leaves and foliar spectral data. Tea polyphenols (TP) and amino acid (AA) and water-soluble protein (SP) are three target chemicals in this paper. Near infrared spectroscopy (NIRS) was also been applied to estimate these chemicals for dried and ground leaves in laboratory. They are compared in terms of retrieval precision. Two main methodologies have been employed for modelling: (a) two bands normalized ratio index (NRI), (b) partial least squares (PLS) regression. The PLS method was performed using the original and transformed spectra: mean centred spectra, standard first derivative and standard normal variate (SNV) transformed spectra. The results demonstrated that the biochemical parameters related to the quality of tea can be estimated with satisfactory accuracy both at dried powder and fresh leaf scales.

As the important component of soil, soil organic matter not only provides every nutrient element for crop, but also has determinant effect for forming of soil structure and melioration the soil physical character. Mapping and dating soil organic matter is of great importance in soil use and evaluation. In this study we examine the feasibility of soil organic matter content by using Hyperspectrally reflective remote sensing methodology. This technique was tested in Xiaochang County located in Hubei province. The soil reflectance properties of samples were measured in the laboratory by ASD field spectrometer. The correlation analysis related with organic matter content was processed from three factors: the spectral reflectance parameter ((lgρ)', ρ/ ρ_450-750 and (1/lgρ₆₂₃)'/ (1/lgρ₅₆₄)'). The results show that the correlation coefficients of r values were: organic matter identification index (ρ/ ρ_450-750) > logarithmic first derivative of reflectivity ((lgρ)') > organic matter mix identification index ((1/lgρ₆₂₃)'/(1/lgρ₅₆₄)'). Knowing these correlations we were able to use the best prominence correlation of organic matter identification index of 1850nm wavelength as the variable regression to build up statistical regression analysis. We used five model types (Linear Function, Logarithmic Function, Quadratic Function, Power Function and Exponential Function) to forecast the soil organic matter content Hyperion model. The accuracy assessment (R²= 0.8484) by relating forecasted organic matter values with Quadratic Function regression showed that the model is reliable and significantly correlative with known stabilization processes throughout the study area. The quantitative methodology developed in this study for refutations soil organic matter content can be adapted to other regions throughout the world.

Leaf Area Index (LAI) is an important parameter in terrestrial ecosystem. Wide width of HJ-1A CCD image causes varied view angels. It is rather important to analyze HJ-1A CCD camera's geometrical features to improve the accuracy of LAI production. This paper focuses on the imaging geometry characteristics of HJ-1A multi-spectral CCD camera, and then brings a self-adaptive approach of retrieving vegetation parameter through analyzing the influence of sensor's imaging geometry factor to vegetation parameter retrieval. This self-adopt approach couples remote sensing imaging process model with vegetation information extraction model and studies systemically on the vegetation information extraction general modeling, data simulation with the remote sensing image features, analyzing the influence of imaging factor to vegetation information extraction and optimizing vegetation information extraction model. The validation result shows that the method in this paper is much better than the simple semi-empirical model no consider of imaging geometrical factor of HJ-1A CCD camera.

SVM (Support Vector Machine) is a new kind of machine learning method , it can solve classification and regression problems very successfully and accomplish classification with small sample incident perfectly. In this paper, the NPA is proposed to compute the optimization problem to achieve the classification for hyperspectral remote sensing (RS) image by "1 VS m" strategy and radial basis kernel function. Besides, a new method, the dual-binary tree + SVM algorithm is proposed, to solve the mutil-class, high-dimensional(HD) problems of hyperspectral RS image. In the end, the test is carried on the OMIS image. The comparative results of this algorithm with other methods are given, which shows that our algorithm is very competitive, particularly for the small samples and non-equilibrium surface features. Both the accuracy and speed of classification are improved greatly.

Basic clustering algorithms including K-means, fusion c-means and C4.5 decision tree have been applied in cloud detection with FY-2C data. With the limitation that no reliable template is available, supervised classification algorithm is utilized to test the credibility of non-supervised classification algorithms, which contribute to generate a cloud classification model with high credibility. It is proved that cloud classification product distributed by National Satellite Meteorological Center enjoys a high credibility and stability. It is also demonstrated that FY-2C data is eligible to classify cloud into 4 types as cumulonimbus, cirrostratus, dense-cirrus and low and middle cloud.

Lagragian support vector machine (LSVM) is a linearly convergent Lagrangian, which is obtained by reformulating the quadratic program of a standard linear support vector machine. To investigate the performance of the classifier working on multispectral images with LSVM as optimizer, we devise a new test based on LSVMs for classifying multispectral data in this work. First of all, data are preprocessed. To acquire the optimum bands for image classification, multispectral image is mapped into a two-dimensional feature space to inspect the bands with redundant spectral information. These extracted data acquired through the feature selection is named data group B relative to the original data group A for a purpose of comparison. Then, to classify multiclass problem, binary classification is extended to multiclass classification by pairwise method. Secondly, two groups of data are trained to find models. In this phase, optimal C values are chosen carefully through trials with different values. Then, classifiers based on LSVMs with optimal C values are used to yield optimal separating hyperplane (OSH). Lastly, in prediction phase, the two groups of data are inputted respectively into each classifier for testing. These classifiers include ones with linear kernel and ones with polynomial kernel of degree 2. The results of the experiment reveal that classifiers with LSVMs as an optimizer have excellent performances with both linear kernel and polynomial kernel of degree 2. Bias caused by the differentia of the two groups of data is not obvious.

High spatial resolution remote sensing images are playing an increasing important role in various applications in the world. As the fundamental work, classification of remote sensing is significant in the applications. This paper proposed a new feature extraction approach based on the shape adaptive neighborhood (SAN) for the classification of high spatial resolution remote sensing images. The heterogeneity based on the color characteristics was employed to determine the SAN of each pixel. Then all the color features, texture features and shape features were extracted from each SAN, and were fused by the feature level data fusion methods to the final feature space of the RS image. Then the features were used to do the classification work. As the experiment results shown, the total precision of the classification was 0.9187, and the kappa coefficient was 0.7950. By analyzing different maximum size of the SAN and different threshold of the heterogeneity, the best maximum size of the SAN was 11*11 for the study area and the most suitable threshold was 0.5.

In the traditional BNC model, the relationship between the attributes are the same for all the instances of the class variable C. BMN classifier is a generalized form of BNC, in the sense that it allows different relationships among attributes for every values of the class variable, and provides a unique net structure for every object class. This paper proposes Bayesian Multi-nets (BMN) Models based on the analysis of conditional mutual information(CMI) between image features of different objects classes, and constructs BMN classifier for remote sensing images on the basis of experiment. Classification accuracy of single objects in BMN classifier outperforms that of traditional BN, proves the latent value of the proposed models in the classification of remote sensing images.

In this paper, we propose a new classification algorithm for high spatial resolution remote sensing data, which makes use of both the spatial information and spectral information of the remote sensing data, and is also suitable for the specific data format that high special resolution remote sensing data has. Because the classification is based on the remote sensing raw data, image fusion process of the raw data can be avoided, which will lower the computational cost. Moreover, the classification algorithm framework we propose can be extended for other segmentation algorithms.

The radial basis function (RBF) neural network is a powerful method for remote sensing image classification. It has a simple architecture and the learning algorithm corresponds to the solution of a linear regression problem, resulting in a fast training process. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBF. Traditional methods to determine the centers are: randomly choose input vectors from the training data set; vectors obtained from unsupervised clustering algorithms, such as k-means, applied to the input data. These conduce that traditional RBF neural network is sensitive to the center initialization. In this paper, the artificial immune network (aiNet) model, a new computational intelligence based on artificial immune networks (AIN), is applied to obtain appropriate centers for remote sensing image classification. In the aiNet-RBF algorihtm, each input pattern corresonds to an antigenic stimulus, while each RBF candidate center is considered to be an element, or cell, of the immune network model. The steps are as follows: A set of candidate centers is initialized at random, where the initial number of candidates and their positions is not crucial to the performance. Then, the clonal selection principle will control which candidates will be selected and how they will be upadated. Note that the clonal selection principle will be responsible for how the centers will represent the training data set. Finally, the immune network will identify and eliminate or suppress self-recognizing individuals to control the number of candidate centers. After the above learning phase, the aiNet network centers represent internal images of the inuput patterns presented to it. The algorithm output is taken to be the matrix of memory cells' coordinates that represent the final centers to be adopted by the RBF network. The stopping criterion of the proposed algorithm is given by a pre-defined number of iterations. The classification results are evaluated by comparing with that of the k-means center selection procedures and other results from the literature using remote sensing imagery. It is shown that aiNet-RBF NN algorithm outperform other algorithms and provides an effective option for remote sensing image classification.

Land use and land cover (LULC) data is essential to environmental and ecological research. However, semantic heterogeneous of land use and land cover classification are often resulted from different data resources, different cultural contexts, and different utilities. Therefore, there is need to develop a method to measure, compare and integrate between land cover categories. To understand the meaning and the use of terminology from different domains, the common ontology approach is used to acquire information regarding the meaning of terms, and to compare two terms to determine how they might be related. Ontology is a formal specification of a shared conceptualization of a domain of interest. LULC classification system is a ontology. The semantic similarity method is used to compare to entities of three LULC classification systems: CORINE (European Environmental Agency), Oregon State (USA), and Taiwan. The semantic properties and relations firstly have been extracted from their definitions of LULC classification systems. Then semantic properties and relations of categories in three LULC classification systems are mutually compared. The visualization of semantic proximity is finally presented to explore the similarity or dissimilarity of data. This study shows the semantic similarity method efficiently detect semantic distance in three LULC classification systems and find out the semantic similar objects.

Texture features are recognized to be a special hint in images, which represent the spatial relations of the gray pixels. Nowadays, the applications of the texture analysis in image classification spread abroad. Combined with wavelet multi-resolution analysis or support vector machine statistical learning theory, texture analysis could improve the quality of classification increasingly. In this paper, we focus on the land cover for the Three Gorges reservoir using remote sensing data SPOT-5, a new classification method, wavelet-SVM classifier based on texture features, is employed for this study. Compare to the traditional maximum likelihood classifier and SVM classifier only use spectrum feature, this method produces more accurate classification results. According to the real environment of the Three Gorges reservoir land cover, a best texture group is selected from several texture features. Decompose the image at different levels, which is one of the main advantage of wavelet, and then compute the texture features in every sub-image, and the next step is eliminating the redundant, every texture features are centralized on the first principal components using principal component analysis. Finally, with the first principal components inputted, we can get the classification result using SVM in every decomposition scale, but what the problem we couldn't overlook is how to select the best SVM parameters. So an iterative rule based on the classification accuracy is induced, the more accuracy, the proper parameters.

In many remote sensing image classification applications, interest focuses on a specific land-cover class. In these cases, one-class classification (OCC) approach is appropriate, because one classifier can be trained with samples of target class and just few or no samples of classes that are not of interest are required. However, it is always hard to build a training sample set effectively to represent the target class completely. In this paper, an active learning is introduced for OCC based on support vector data description (SVDD). In active SVDD learning, a SVDD classifier is started with a small size of training samples and an expert is asked to label supplementary training data by asking only for the labels of the most informative, unlabeled examples. Thus, it is possible to build a training sample set effectively to represent the target class completely. The effectiveness of active SVDD is proved by preliminary experiments.

In so far as geometric shape is concerned a curvilinear band is very similar to a road segment. Roads may be regarded as a particular type of curvilinear bands. Many methods have been developed for extracting such targets, some of which are based on edge information. It is very difficult to extract roads and curvilinear bands with no clear and continuous boundaries using existing algorithms. To mitigate this problem a new strategy is presented in this paper. Edge lines are detected by means of an improved Burns' method and Dynamic Programming. Target segments are then formed by matching these edge lines. Connecting the obtained segments produces the final results. Experiments prove the efficiency of the proposed method while being applied to extract complicated roads and curvilinear bands in different kinds of images.

Imaging spectroscopic technique has been used for the mineral and rock geological mapping and alteration information extraction successfully with many reasonable results, but it is mainly used in arid and semi-arid land with low vegetation covering. In the case of the high vegetation covering, the outcrop of the altered rocks is small and distributes sparsely, the altered rocks is difficult to be identified directly. The target detection technique using imaging spectroscopic data should be introduced to the extraction of small geological targets under high vegetation covering area. In the paper, we take Ding-Ma gold deposit as the study area which located in Zhenan country, Shanxi province, the spectral features of the targets and the backgrounds are studied and analyzed using the field reflectance spectra, in addition to the study of the principle of the algorithms, some target detection algorithms which is appropriate to the small geological target detection are introduced. At last, the small altered rock targets under the covering of vegetation in forest are detected and discriminated in imaging spectroscopy data with the methods of spectral angle mapper (SAM), Constrained Energy Minimization (CEM) and Adaptive Cosine Estimator (ACE). The detection results are reasonable and indicate the ability of target detection algorithms in geological target detection in the forest area.

This paper presents a BP neural network method to retrieve vegetation cover of the Drainage basin of Miyun Reservoir based on the new remote sensing data - Beijing-1 microsatellite data. And the retrieval result was compared with the one retrieved by the traditional methods. The compared results showed that for vegetation cover retrieval in mountainous areas, neural network has the advantage of precise simulation of non-linear transmission over traditional methods, especially for mountainous vegetation cover retrieval which traditional methods usually fail.

Hyperspectral and high spatial resolution remote sensing technology take important role in uranium geological application, data mining and knowledge discovery methods are key to character spectral and spatial information of uranium mineralization factors. Based on curvelet transform algorithm, this paper developed the image fusion technology of hyperspectral (Hyperion) and high spatial data (SPOT5), and results demonstrated that fusion image had advantage in denoising, enhancing and information identification. Used discrete wavelet transform, the spectral parameters of uranium mineralization factors were acquired, the spectral identification pedigrees of typical quadrivalence and hexavalence uranium minerals were established. Furthermore, utilizing hyperspectral remote sensing observation technology, this paper developed hyperspectral logging of drill cores and trench, it can quickly processed lots of geological and spectral information, and the relationship between radioactive intensity and abnormal spectral characteristics of Fe³⁺ was established. All those provided remote sensing technical bases to uranium geology, and the better results have been achieved in Taoshan uranium deposits in south China.

This paper aims at the extraction of roads and road network from high-resolution dual-polarization synthetic aperture radar data over urban areas. According to the different features and applications for road network, the errors will be brought in the detection algorithm if it was not selected correctly. We proposed a modified extraction method making full use of available information to reduce such errors. In particular, we want to show how to implement road extraction algorithms based on the D-S evidence theory to establish the frame of discernment. The responses of two line detectors at the local analysis process were combined, which was done at the feature level by balancing the weight of two propositions constructed by responses of two line detectors. Then the road network optimization is accomplished using a Markov random field model of road, where both some contextual knowledge and global constraints were taken into account. The experimental results indicate that the proposed method is promising for main roads detection of urban areas.

In this paper we focus on feature extraction method of complex objects from range data captured by vehicle-borne laser scanning system. In our study, we classified the objects obtained by laser scanner into three types: road surface, building facade and free sharps which consisted by scattered points. We have developed the corresponding methods for the extraction of those objects. The DSCD (Density Statistics with Conformal Division) for road surface extraction and DSEI (Density Statistics with Equal Interval) for building facade extraction. Especially, our experiment has shown that the MST (Minimum Spanning Tree) is a good way for scattered points classification, and the free sharps are extracted with high automation and efficiency.

Tree height is an important biophysical parameter. It is a need for forest and biomass map for the estimation of carbon budget. Polarimetric SAR interferometry (PolInSAR) has been applied successfully to retrieve biophysical parameters from forest areas. In the real process, the retrieval result is not truthfulness because of non-volumetric scattering decorrelation scatterer. The scatterer is generated by coherence noise. The scatterer will be misjudged and mistook for a tree. Before Retrieving of Tree Height, non-volumetric scattering decorrelation scatterer needed to be removed. This paper will propose to combine the linear and optimization polarization interferometric coherence to remove the nonvolumetric scattering decorrelation scatterer. The retrieval results have been proved reasonable.

In this paper, a multi-level image representation model is developed and used for multi-spectral remote sensing image retrieval in order to narrow the gap between the low-level feature and high-level semantic. This model consists of an image segmentation part, a feature extraction part and semantic extraction part. The first two parts aim at the extraction of primitive region feature of an image. In these two steps, an improved JSEG algorithm is used to segment the image stored in the database, then spectral feature and texture feature are extracted for each region. In semantic extraction part, the semantic information hidden in different regions of different images is extracted by Bayesian method and expectation maximization (EM) method. At last, positive example and negative example concept is used in image retrieval instead of relevant feedback. Experiment shows that this method not only improves the accuracy of the result but also decreases the complexity of retrieval.

The automatic extraction of roads from high spatial resolution remote sensing images is the hotspot and difficulty in remote sensing research, and has attracted extensive attention. Road extraction from remote sensing images is a leading direction of remote sensing image processing and widely demanded in transportation, mapping, urban planning and other fields. In this paper, the actuality of road extraction is investigated. The road extraction process is divided into five phases, which are pre-processing, low-level processing, mid-level processing, high-level processing and the application of extraction results. The methods used in each phase are analyzed. Low-level processing is considered to be the key and foundation of road extraction. Based on the representative method in low-level processing, mathematical morphology, this paper presents an approach for automatic road extraction and tests the method on remote sensing image. The experimental result shows the efficiency of the presented approach.

The abundance of high resolution image information and the intensity of urban spatial system can be combined organically in the process of image understanding, information extraction and quota measurement. The evaluation indices of urban land use intensity extracted from Quickbird image include building density, floor ratio area, green ratio, vacancy ratio, and etc. Firstly, land use condition in the research area is acquired through the overlay of Quickbird image and Wuhan land use map. Secondly, the study adopts spectral threshold segmentation method to extract building shadow, object-oriented classification method to obtain building base area, shadow-based height reversion approach to estimate building height in typical urban residential block and object-oriented segmentation and classification approach to estimate concerned indices in city village. In the end, the comparison and discussion of urban land intensity are made according to BD and FAR in urban residential block and city village respectively. It is concluded that FAR (floor ratio area) in urban residential block can be planned higher and BD (building density) in city village should be lower according to the present planning regulations in Wuhan.

ADS40 from Leica Geosystems is new-type airborne digital camera, which has aggregated worldwide focus in application fields of aerial photogrammetry and remote sensing nowadays, with its noticeable superiority in interpreting and mapping the comprehensive conditions of ground surface. At the same time, its special characteristics make the data processing some complicated. Based on the long-team research and the experiment on usage of ADS40, the paper presented the workflow in details including data capture, processing and application of ADS40, described some problem and exercise in practice.

Accurate atmospheric correction is an important and essential process in ocean color remote sensing because the influence of atmosphere account for the main part of signals received by sensors. Traditional methods usually depend on in-situ measured parameters of atmosphere and could not be applied in operational system. In this paper, MODIS products synchronize with Beijing-1 micro-satellite image were used to extract the parameters of atmosphere. we chose a marine space of clean water far away from the coast in MODIS image and used the products include MOD02, MOD03 and MOD07 to calculate the aerosol radiance of near-infrared bands of MODIS which were used to extrapolate the aerosol radiances of each band of Beijing-1 micro-satellite. Brought the results into radioactive transfer equation and fulfilled atmosphere correction. We found this method can enhanced the detail information of water body, especially to case 2 water. We compared the correction results with original image and the results from 6S model; its effect was consistent well with real conditions and better than 6S model. All these indicated that this method is feasible to atmospheric correction of turbid coastal waters and expands the application of multi-spectral sensors in ocean color remote sensing.

The hyperspectral reflectance for rape fresh leaves and data of chlorophyll and total nitrogen content were acquired in primary growth stages under different nitrogen levels in order to monitor rape status and diagnose nitrogen using remote sensing method. A new method was developed for estimating the nitrogen nutrition of rape using continuum-removed method, which generally used in spectral analysis on rock and mineral. Based on the continuum-removed treatment and the correlation between absorption feature parameters and total nitrogen content of fresh leaves, results show that reflectance at the visible region decreased with increasing in the nitrogen fertilization, and continuum-removed operation can magnify the subtle difference in spectral absorption characteristics arose from the nitrogen stress on rape. During the seeding stage, bud-emerging stage and flowering stage of rape, total area of absorption peak, area left of the absorption peak and area right of the absorption peak in 550-750 nm region increased with increasing in the nitrogen fertilization, but it was opposite for the area-normalized maximal absorption depth. The correlation analysis indicated that it is at seeding stage that the relation between absorption characteristics parameters and leaf total nitrogen was best close. The research demonstrated that continuum-removed method is a feasible method for quantificational evaluation of rape nitrogen nutrition, and the seeding stage of rape is the best stage for assessment of rape nitrogen nutrition based on absorption characteristics of fresh leaves.

This paper extended the best tradeoff fast intensity-hue-saturation (BTFIHS) fusion to a more general model. Firstly, based-on the methods proposed by previous researches, we integrated injecting strategy of spatial detail information into the fusion model and also adopted the idea of BTFIHS to keep the intensity unchanged with the corresponding pixel in panchromatic (PAN) image, while the hue and saturation of every pixel using BTFIHS were equal to them using tradeoff fast intensity-hue-saturation (TFIHS). Then we got a general formula of the improved best tradeoff for high-resolution image fusion based-on getting injecting parameter by spectral reflection function (SRF). At last, several experiments were carried on to analysis and discuss the quality of the new methods compared with their originals. Results show that the new method could yield a "true" high-resolution multispectral (MS) image, with vast improvement in blue band and nearly 3.3% improvement in Q4 for all bands.

The ADS40 system is a multi-line-array digital photogrammetry system. It takes the photo of terrain by pushbroom scanning. Highly integrated with POS, it can obtain the orientation elements for every scan line through the post-process of POS. For the existence of system errors of GPS and IMU, the Orientation elements may not satisfy the highly precise application. It can be expressed with the function of time. In order to improve the accuracy of the orientation elements of each scan line, Aerial triangulation must be used. We set out from the principle of photogrammetry, analyze the imaging principle of ADS40, and put forward the imaging equations of ADS40 and the error equations of POS. We discuss on the relationship between the orientation elements of each scan line and time, and form the function of the orientation elements of each scan line and time. By introducing the conception of orientation fix we derive the adjustment math model which include the orientation elements obtained through the post-process of POS. Therefore we may illuminate the system errors or occasional errors of each observed value by adjustment, and achieve the aim to raise the accuracy of orientation elements.

Synthetic aperture radar (SAR) is an active remote sensing sensor. It is a coherent imaging system, the speckle is its inherent default, which affects badly the interpretation and recognition of the SAR targets. Conventional methods of removing the speckle is studied usually in real SAR image, which reduce the edges of the images at the same time as depressing the speckle. Morever, Conventional methods lost the information about images phase. Removing the speckle and enhancing the target and edge simultaneously are still a puzzle. To suppress the spckle and enhance the targets and the edges simultaneously, a half-quadratic variational regularization method in complex SAR image is presented, which is based on the prior knowledge of the targets and the edge. Due to the non-quadratic and non- convex quality and the complexity of the cost function, a half-quadratic variational regularization variation is used to construct a new cost function,which is solved by alternate optimization. In the proposed scheme, the construction of the model, the solution of the model and the selection of the model peremeters are studied carefully. In the end, we validate the method using the real SAR data.Theoretic analysis and the experimental results illustrate the the feasibility of the proposed method. Further more, the proposed method can preserve the information about images phase.

The orthometric height system plays a key role in geodesy, and it has broad applications in various fields and activities. On an arbitrary equigeopotential surface, there does not exist the frequency shift of an electromagnetic wave signal. However, between arbitrary two different equigeopotential surfaces, there exists the frequency shift of the signal. Just due to this principle of nature, one can determine the geopotential difference as well as the orthometric height difference between two separated points P and Q using electromagnetic wave signals, especially the GPS signals. GPS signals with a definite frequency f are emitted and two receivers at P and Q on ground receive the signals coming from the emitter simultaneously. The frequencies of the signals are recorded by receivers at P and Q, and consequently the frequency difference (shift) between the received frequencies of the signals at P and Q is determined. Then, the geopotential difference between these two points is determined based on the geopotential frequency shift equation, and the corresponding orthometric height difference is further determined based on the Bruns' formula. Further, using this approach a unified world height datum could be realized, because P and Q could be chosen quite arbitrarily, e.g., they are located on two separated continents or islands.

This paper introduce a new method for rapid extracting the planar features by integrating their point cloud and digital images, the method is more efficient than any planar feature extracting methods based on one of the both type data and can get the accurate 3D object boundaries. The main idea and the basic data processing flow about the presented method are discussed in detail, and a test with the data of a building side face is employed to analyzing the efficiency and the accuracy of the presented method. Some conclusions and future work are given in the last section.

C factor, known as cover and management factor in USLE, is one of the most important factors since it represents the combined effects of plant, soil cover and management on erosion, whereas it also most easily changed variables by men for it itself is time-variant and the uncertainty nature. So it's vital to compute C factor properly in order to model erosion effectively. In this paper we attempt to present a new method for calculating C value using Vegetation Index (VI) derived from multi-temporal MODIS imagery, which can estimate C factor in a more scientific way. Based on the theory that C factor is strongly correlated with VI, the average annual C value is estimated by adding the VI value of three growth phases within a year with different weights. Modified Fournier Index (MFI) is employed to determine the weight of each growth phase for the vegetation growth and agricultural activities are significantly influenced by precipitation. The C values generated by the proposed method were compared with that of other method, and the results showed that the results of our method is highly correlated with the others. This study is helpful to extract C value from satellite data in a scientific and efficient way, which in turn could be used to facilitate the prediction of erosion.

This paper discusses a new method for the automatic matching of ground control points (GCPs) between satellite remote sensing Image and digital raster graphic (DRG) in urban areas. The key of this method is to automatically extract tie point pairs according to geographic characters from such heterogeneous images. Since there are big differences between such heterogeneous images respect to texture and corner features, more detail analyzations are performed to find similarities and differences between high resolution remote sensing Image and (DRG). Furthermore a new algorithms based on the fuzzy-c means (FCM) method is proposed to extract linear feature in remote sensing Image. Based on linear feature, crossings and corners extracted from these features are chosen as GCPs. On the other hand, similar method was used to find same features from DRGs. Finally, Hausdorff Distance was adopted to pick matching GCPs from above two GCP groups. Experiences shown the method can extract GCPs from such images with a reasonable RMS error.

New technologies and automated systems (such as multi-sensor systems) allow us to collect and store a large amount of spatial data in a quite efficient and inexpensive way. Especially, the advent of remote sensing and GIS has great enhanced our capabilities to capture spatial data. However raw data are seldom useful without some kind of processing, it needs more powerful technologies to deal with the databases. Therefore, spatial data fusion and data mining have been used in this domain. They can improve the efficiency and accuracy of spatial information utilization. In this paper, we focused on how to fusion spatial data for decision making by learning Bayesian networks. A review is presented on spatial data fusion. We propose a method of spatial data fusion based on Bayesian networks, which is optimized by using the theory of Particle Swarm Optimization (PSO). And then we showed a case study for spatial data fusion based on the approach. The experimental results are given to illustrate the practical feasibility of the proposed technique. Eventually, we conclude with a summary and a statement of future work.

The objective of this study is to exploit the new features of ALOS PALSAR dual polarization mode data and to develop novel classification method for forest mapping in heterogeneous areas. A test site was selected in Fujian province in southeast of China. Traditionally, forest is detected by its low coherence, low temporal variability of the backscattering intensity and mediate backscattering intensity. However, the analyses in this paper indicate that it is not possible to discriminate forest from nonforest by any single PALSAR feature in this test site. After examination the dependences of the multitemporal backscatter intensity, the polarimetric parameters and the interferometric coherence on different land cover types, a hierarchical classification method is proposed for coastal forest and hilly forest mapping. The forest maps are validated by forest inventory data and SPOPT-5 images. The results show that multitemporal PALSAR dual polarization data can accurate maps for coastal forest in flat areas using the proposed method. The capability to map forest in hilly regions is still limited.

In order to raise the intelligent level and improve cooperative ability of grid. This paper proposes an agent oriented middleware, which is applied to the traditional OGSA architecture to compose a new architecture named CIG (Cooperative Intelligent Grid) and expounds the types of cooperative processing of remote sensing, the architecture of CIG and how to implement the cooperation in the CIG environment.

An improved method of wavelet threshold denoising is introduced and applied to hyperspectral imagery denoising in spectral domain. This method estimates a threshold value for each spectrum. Thresholds are set to a scalar specifying the percentage of cumulative power to retain in the filtered wavelet transform. Find the actual percent corresponding to these coefficients. During the processing, four families of mother wavelets (Symlets, Daubechies, Haar and Coiflet) are tested in a series of experiments to estimate the functioning of those wavelets and thresholding parameters. Experimental results show that the proposed algorithm with Coiflet provides an improvement in SNR for hyperspectral data specially.

Laser scanning is an effective way to acquire geometry data of the cultural heritage with complex architecture. After generating the 3D model of the object, it's difficult to do the exactly texture mapping for the real object. we take effort to create seamless texture maps for a virtual heritage of arbitrary topology. Texture detail is acquired directly from the real object in a light condition as uniform as we can make. After preprocessing, images are then registered on the 3D mesh by a semi-automatic way. Then we divide the mesh into mesh patches overlapped with each other according to the valid texture area of each image. An optimal correspondence between mesh patches and sections of the acquired images is built. Then, a smoothing approach is proposed to erase the seam between different images that map on adjacent mesh patches, based on texture blending. The obtained result with a Buddha of Dunhuang Mogao Grottoes is presented and discussed.

The development of remote geological interpretation technology is booming during recent years. However, there is a significant obstacle for extracting geology information from remote sensing imagery--the presence of clouds and their shadows. Diverse techniques have been proposed including different algorithms such as filtering algorithm and multi-temporal cloud removing algorithm to solve the problem. This paper presents a modified solution to denoise the haze, based on ETM+ imagery. First of all, wavelet transform is applied to Band1, Band2 and Band3 imagery to determine the clear region and different levels of cloud regions. Then all pixels of the ETM+ imagery are classified to specific cover types after the cluster analysis of band4, Band5 and Band7. At last, the mean reflectance matching is performed in the first three bands separately according to different cover types in both clear region and cloud region. Above all, the method is implemented by IDL. The results show that this modified method not only can quantitatively determine the cloud area but also can remove cloud from imagery efficiently. Moreover, compared with the homomorphic filtering method, the experiment results of the proposed method is much more satisfying in Geology Interpretation.

Among all enhancement techniques being developed over the past two decades, anisotropic diffusion has received a lot of attention and has experienced significant developments, with promising results and applications in several specific domains. The elegant property of the technique is that it can enhance images by reducing undesirable intensity variability within the objects in image, while improving signal-to-noise ratio (SNR) and enhancing the contrast of the edges in scalar and, more recently, in vector-valued images, such as color, multispectral and hyperspectral imagery. In this paper, we firstly analyze two complementary schemes-variational methods and nonlinear diffusion partial differential equations (PDEs), in terms of edge enhancement. Based on these analyses, a general flexible class of hyperspectral forward-and-backward (FAB) diffusion process will be proposed, which can achieve the main requirements for edgepreserving regularization with image enhancement. In addition, we use additive operator splitting (AOS) scheme to speedup the numerical evolution of the nonlinear diffusion equation with respect to traditional explicit schemes. The performance of the vector-valued FAB diffusion PDE is studied using some hyperspectral remote sensing images. Experimental results on these images are shown the validity and effectiveness of the proposed method.

The study on epipolarity model of satellite stereo-imagery has been the research focus in application field of remote sensing for a long time. So far, there are basically two technical routines for exploring the epipolarity model of satellite images: one totally disregards any geometric sensor model when establishing the epipolar relations but figures out the epipolar curves in image-space by image correspondence and mathematical modeling, e.g. the Polynomial Fitting Model (PFM); on the contrary, the other attempts to exploit the epipolar relations based on specific sensor models, such as the Projection Trajectory based Epipolarity Model (PTEM), the Parallel Projection Transformation based Epipolarity Model (PPEM), etc. Although such models have been studied and used to generate epipolar images of satellite stereo pairs, some technical limitations still exist when taking the universality, applicability and implemental simplicity into account. Accordingly, this paper proposes a novel epipolarity model for satellite stereo-imagery based on the Virtual Horizontal Plane (VHP) of object-space. Firstly, the principle of the VHP-based epipolarity model is described; and then, the workflow of VHP-based epipolar resampling is outlined in detail; finally, to verify the feasibility and correctness of new theory and method, the approximate epipolar images of SPOT5-HRG stereo-imagery are generated. It is demonstrated that the vertical parallaxes of conjugate image points have all reached the sub-pixel level after epipolar resampling; besides, by rearranging the approximate epipolar lines on the VHP defined in our method, the stereoscopic model that is horizontal to the object-space and with consistent resolution can be available.

This paper introduces three methods of eliminating shadow on airborne SAR images, which are increasing overlap ratio, imaging on both sides and imaging on across orientation. Reason and factors of causing shadow is analyzed in this paper. The effect of eliminating shadow using the three methods mentioned is analyzed quantitatively. Given the side-looking angle is from 20 degree to 70 degree. When the slope of reversing side-looking orientation is smaller than 70 degree, if SAR images' overlap ratio is 75%, shadow incidence can be reduced to the slope from 57.5 degree to 70 degree. At the same time, if imaging on both sides is used, shadow can be eliminated absolutely when the slope of one side is between 57.5 degree to 70 degree, and shadow can be eliminated in the condition of vale bottom's width is more than the threshold value mentioned in this paper when the slopes of both sides are between 57.5 degree to 70 degree. When the slope of reversing side-looking orientation is bigger than 70 degree and vale bottom's width is less than the threshold value mentioned in this paper, air line which flight track is across vale can be adopted in order to avoiding shadow effect. The reasons caused shadow by terrain and eliminating methods used are analyzed in Hengduan Mountain area, air lines are designed in this area for obtaining airborne SAR images based on above.

An algorithm for satellite tracking and orbit prediction is presented in detail. Firstly, the satellite's position and velocity are calculated by the Simplified General Perturbations Version 4 and Simplified Deep-space Perturbation Version 4 (SGP4/SDP4) orbit propagation algorithms. And we put forward "Two-Point-Pair" which means two points that the CCD of satellite's IFOV rays intersecting with the earth. We make use of the "Two-Point-Pair" to calculate the accuracy bounding box of satellite at the instantaneous time through the satellite's position and velocity above. Besides, we build a system called GeoGlobe to simulate the algorithm. The system can be used to simulate all kinds of satellites that the Two-Line Element (TLE) Sets files can provide.

This paper presents a universal maximum a posteriori (MAP) based reconstruction method which can be used for destriping, inpainting (the removal of dead pixels) and super resolution reconstruction (the recovery of a high resolution image from several low resolution images). In the MAP framework, the likelihood probability density function (PDF) is constructed based on a linear image observation model, and a robust Huber-Markov model is used as the prior PDF. A gradient descent optimization method is employed to produce the desired image. The proposed algorithm has been tested using MODIS images for destriping and super resolution reconstruction, and CBERS (China-Brazil Earth Resource Satellite) and QuickBird images for simulated inpainting. The experiment results and quantitative analyses verify the efficacy of this algorithm.

Image registration is a fundamental process in many remote sensing applications, such as image fusion, temporal change detection. Generally, Algorithms for image registration can be classifeed into two categories: featurebased and area-based methods. feature-based methods are relatively fast, robust and reliable, and area-based methods can get high accuracy with high computational cost. Because the result produced by traditional feature detector may vary with image contrast, it is dificult to set appropriate thresholds automatically for the reference image and sensed image. To solved these problem, an automatic approach for image registration is presented in this paper. It use a feature-based approach to get a coarse registration at first. Then, area-based method used to improve the accuracy of the result. In feature detection stage, it employs a feature detector implemented in frequency domain to obtain features with normalized measure. Constant thresholds can be applied for different images. Due to feature matching is time-consuming and computation expensive, line features detected from the images with approximate direction are mapped into Hough space to estimate the transformation parameters with Modified Iterative Hough Transform method. Furthermore, it use a hierarchical framework to speed up the registration process. The experiments show that the approach mentioned above is feasible and efficient.

Segmentation of Point cloud data is a key but difficult problem for architecture 3D reconstruction. Because compared to reverse engineering, there are more noise in ancient architecture point cloud data of edge because of mirror reflection and the traditional methods are hard that is not fuzzy in the preceding part of this paper, these methods can't embody the case of the points of borderline belonging two regions and it is difficult to satisfy demands of segmentation of ancient architecture point cloud data. Ancient architecture is mostly composed of columniation, plinth, arch, girder and tile on specifically order. Each of the component's surfaces is regular and smooth and belongingness of borderline points is very blurry. According to the character the author proposed a modified Fuzzy C-means clustering (MFCM) algorithm, which is used to add geometrical information during clustering. In addition this method improves belongingness constraints to avoid influence of noise on the result of segmentation. The algorithm is used in the project "Digital surveying of ancient architecture--- Forbidden City". Experiments show that the method is a good anti-noise, accuracy and adaptability and greater degree of human intervention is reduced. After segmentation internal point and point edge can be districted according membership of every point, so as to facilitate the follow-up to the surface feature extraction and model identification, and effective support for the three-dimensional model of the reconstruction of ancient buildings is provided.

Image segmentation is essential for information extraction from remote sensing image, but it remains the lacks of a general mathematical theory, object merging for poor object boundary localization, dealing with object fragmentation and sensitivity of current procedures to noise. This paper focuses on hyper-spectral image segmentation using probabilistic neural networks (PNN). The methodology, implementation and optimization of a PNN are studied, and a constructed PNN is applied to segment hyper-spectral image. The experience demonstrates main advantage of a PNN that it has quick training and learning, gives a measurement of confidence associated with an output, and has the ability to process large data set. It is concluded that the PNN is superior in image segmentation and the obtained results are satisfied.

In this paper, a new texture segmentation approach based on Markov random field (MRF) and global optimal method of particle swarm optimization (PSO) is proposed. According to this approach, firstly the MRF texture model is established, and potential function of Gibbs distribution and the calculating method of Gibbs parameters are represented. Then the fitness function is designed and the PSO is adopted here to solve the maximum a posterior (MAP) estimate. Finally, a comparison of the new algorithm with the Metropolis algorithm and the Gibbs Sampler is made in texture segmentation of remote sensing images. Results show that PSO algorithm can reduce the computational complexity and is much more efficient.

We propose an efficient, hybrid Fourier-Contourlet regularized deconvolution (ForCorRD) algorithm that performs noise regularization via scalar shrinkage in both the Fourier and Contourlet domains. It is based on and more efficient to the famous Fourier-Wavelet regularized deconvolution (ForWaRD) algorithm. The Fourier shrinkage exploits the Fourier transform's economical representation of the colored noise inherent in deconvolution, whereas the contourlet shrinkage exploits the contourlet domain's economical representation of piecewise smooth signals and images. Like the ForWaRD algorithm, ForCoRD is also applicable to all ill-conditioned deconvolution problems. In the same experiment condition for nature images' debluring, we prove that ForCoRD outperforms ForWaRD's in terms of both visual quality and PSNR performance.

The performance of high-resolution imaging with large optical instruments is severely limited by atmospheric turbulence. Image deconvolution such as iterative blind deconvolution (IBD) and Richardson-Lucy (RL) deconvolution are required. The IBD method involves the imposition of constraints such as conservation of energy, positivity, and finite support, with known size, alternately on the image and the PSF in the spatial and Fourier domains, until convergence. The iterative RL solution converges to the maximum likelihood solution for Poisson statistics in the data. Properties of the RL algorithm which make it well-suited for IBD are energy conservation and the sustenance of nonnegativity. So, RL was incorporated into the IBD framework. In this paper, an enhanced Richardson-Lucy-based iterative blind deconvolution (ERL-IBD) algorithm is proposed to restore the blurred images due to atmospheric turbulence. The ERLIBD incorporates dynamic PSF support estimation, bandwidth constraint of optical system, and the asymmetry factor update. The experimental results demonstrate that the ERL-IBD algorithm works better than IBD algorithm in deconvolution of the blurred-turbulence image.

We always suffer the speckle noise when SAR image is used. In order to facilitate image interpretation, finding a speckle reduction method is important and has practical significance. We propose a method which uses fractal net evolution segmentation approach to find homogenous region to replace the traditional rectangular window. The experiment proves our method is more effective than the other traditional methods.

Along with the rapid development of modern space science, it is becoming a hot topic to use network spacecrafts for deep space detection or earth observation. And that autonomous relative navigation is one of key technologies for network spacecraft system. In this paper, we now propose a new autonomous relative navigation algorithm for network spacecraft system with quaternion. In this abstract, we just add some pertinent remarks to listing the main work of this paper: (1) a 3-D line and its transformation can be described by using dual quaternion finely. Moreover, the algorithm with dual quaternion is used to calculate both relative position and attitude organically. So in the first topic section, how to depict a 3-D line and its transformation with dual quaternion will be introduced; (2) in second topic section, the thought of how to select the state valuable will be expatiated here firstly, and then we built the state equation state equation according to Clohessy-Wiltshire (C-W) equation and quaternion differential equation; (3) in this paper, the state valuable and the observation valuable are inconsistent for the observation unit is feature line. So how to build the relationship between the state valuable and the observation valuable is very important. Here through the observation transition matrix, we build the observation equation according to con-line equation of vision navigation; (4) finally, we give an example about network spacecraft system. The simulation results show that proposed algorithm is valid for network spacecraft system.

A very important factor for recovering the high accuracy Earth's gravity field is the knowledge of the GOCE satellite orbit. In this paper, a 30-day arc of the GOCE satellite orbit (called the reference orbit) was simulated using dynamic orbit integration method, and the perturbation accelerations due to the lunisolar perturbations, the Earth tides, the ocean tides, the atmospheric tides, the pole tides, the relativity effects, the atmospheric drag and the solar radiation pressure were computed along the simulated orbit according to the dynamic models. Then the maximum perturbation accelerations and their percentage contribution in the sum of all accelerations due to the aforementioned forces were given and analyzed. In addition, the various variants of the satellite orbit (called the modified reference orbits) were obtained by subtracting the selected conservative forces to the satellite motion model for the reference orbit. Finally, the motion of GOCE satellite that influenced by the conservative forces were analyzed through comparing the satellite positions between the reference orbit and the modified reference orbits. The analysis results can be as a reference for the precise orbit determination and gravity field recovery of GOCE mission.

Space Very Long Baseline Interferometry (SVLBI) is the unique space technique that can directly interconnect the main three reference systems for geodesy and geodynamics. However, the estimable sequence of geodetic parameters including nutation parameters within SVLBI mathematical model has not been determined yet. In this paper, using the mathematical model of space-ground SVLBI observations including the nutation parameters derived by WEI Erhu et al.(2008), the estimable parameter sequence is determined. And the same study is done with space-space SVLBI Observations. To study the standard deviation of nutation parameters estimated with space-ground SVLBI observations, the model of variance propagation is derived, with which some numerical tests are done. Finally, the results are present.

The new generation of space datum in China should comply with the latest IERS (International Earth Rotation and Reference System Service) convention as much as possible. This paper has deeply addressed and researched the main improvements of the current version IERS Conventions (2003), including the changes of the Terrestrial Reference System, the Celestial Reference System, and the transformation between them, the tide correction and so on, which would undoubtedly benefit the realization and maintenance of our space datum. Based on PANDA (Position And Navigation Data Analyst) software developed by GNSS Center of Wuhan University, we analyzed the effect of improvements of the IERS Conventions (2003) on precise orbit determination and precise positioning. The results show that the effect of improvements of models of the coordinate transformation between the celestial and the terrestrial reference system and tide correction (including solid earth tide, ocean tide and polar tide) on precise orbit determination are 4mm, 9mm and 5mm in terms of RMS in along, cross and radial direction of the track; and the effect of the improvement of the tide models on positioning is basically under 0.6mm, and the RMS of the differences are 0.3mm, 0.3mm and 0.2mm in X, Y and Z.

Under the financial support of several Chinese national scientific projects, PANDA (Positioning And Navigation Data Analyst) software developed originally by Wuhan University has achieved the advanced level in the world. PANDA is currently recognized as a main research tool in several famous institutes in the GNSS community. In this paper, the recent development of PANDA software is introduced, including the COSMIC orbit determination in low Earth orbits, the real-time GPS satellite orbit and clock determination and precise point positioning with ambiguity resolution. It is concluded that PANDA is of great improvement in the past five years, and more advancement will be made in its pragmatic aspect especially in engineering applications.

In this paper, the methods of earth rotation parameter (ERP) estimation based on IGS SINEX file of GPS solution are discussed in details. To estimate ERP, two different ways are involved: one is the parameter transformation method, and the other is direct adjustment method with restrictive conditions. With the IGS daily SINEX files produced by GPS tracking stations can be used to estimate ERP. The parameter transformation method can simplify the process. The process result indicates that the systemic error will exist in the estimated ERP by only using GPS observations. As to the daily GPS SINEX files, why the distinct systemic error is exist in the ERP, or whether this systemic error will affect other parameters estimation, and what its influenced magnitude being, it needs further study in the future.

Real-time high-precision GPS positioning and navigation requires that cycle-slip in the undifferenced carrier-phase measurements can be detected instantaneously. A slip of only a few cycles can bias measurements enough to make centimeter-level positioning or navigation difficult. Over the past decade a number of methods have been developed to detect and repair cycle slips. The majority of methods invariably are used in the post-processing cycle-slip detection. A method has been developed from various exiting techniques, that provides real-time cycle-slip detection (i.e., using only current epoch's GPS carrier-phase measurements). The approach utilizes two linear combinations, the Geometry-free and the Melbourne-Wübbena combination. The low degree polynomial fitting and running-average filter are used to detect cycle slips. Simulation tests are conducted to the kinematic data. Results indicate that single-cycle slips can be reliably detected instantaneously.

The high precision relative positioning is one of the key conditions of multiple earth-observation satellites differential observation. Usually, the earth-observation satellites' relative positions are got by GNSS differential positioning. The key technology of GNSS high precision differential positioning is carrier phase AR (Ambiguity Resolution). The distance between two earth-observation satellites is from several kilometers to several hundreds kilometers. So it's a problem of ambiguities resolution for mid-long distance baseline. There are a lot of difficulties to solve this kind of ambiguities such as large double difference systematic errors. In order to solve these difficulties, this paper proposed a fast and kinematic AR method named as CPES (Coordinate Parameters Eliminated and Stepwise) method which is based on LAMBDA method. At first, the primary theories of this method are introduced. Then, the steps from wide-lane ambiguities resolution to L1, L2 ambiguities resolution are proposed. Lastly, several examples' results show that this AR method has the advantages of fast speed and high reliability.

In this paper, atmospheric profiles of refractivity, pressure and temperature derived from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) radio occultation (RO) mission are compared statistically with collocated high-vertical-resolution soundings of 38 radiosonde stations distributed over Australia and the Antarctica during the period from July 15, 2006 to April 30, 2008. Refractivity comparison results show that through 0~30 km, the mean absolute fractional refractivity differences are less than 0.5%, but the refractivity standard deviations vary greatly. The smallest refractivity standard deviation of less than 1% is got at about 10km and the largest standard deviations are found below 5km. It is shown in the pressure comparison results that the mean absolute fractional pressure differences are generally less than 0.5% between 5 km and 25 km. Below 5 km, the mean absolute fractional pressure differences increase to be about 2%. Temperature comparison results show that the mean absolute temperature differences are generally less than 1K and the standard deviations generally less than 2K in the middle to upper atmosphere. In the lower troposphere, the temperature standard deviation increases to be larger than 2.5K. It can be concluded that the precision of GPS RO data from COSMIC are equivalent to radiosonde data in the upper troposphere and lower stratosphere. Being able to complement radiosonde network over the oceans and polar regions, GPS RO data are of great value for climate monitoring.

Some Orbit Determination (OD) of Low Earth Orbiters (LEOs) based on undifferenced spaceborne GPS data were discussed firstly in this paper. Then the principle and mathematical models of two different types of reduced-dynamic were present. After that, dual-frequency spaceborne GPS data of doy 89, 2004 from CHAMP and GRACE satellite were computed using two types of reduced-dynamic POD and the OD results were analyzed. Our CHAMP orbiting results of one day using two different reduced dynamic POD methods are within 7 centimeters compared with GFZ Post processed Science Orbits (PSO) and the GRACE orbiting results are with 3 centimeters compared with JPL OD results.

The paper puts forward a new method of real-time precise GPS satellite clock offset estimation. The method adapts the Square Root Information Filter (SRIF) that has benefits of the numerical stabilization, also eliminates the ambiguity parameter through the difference of the epoch which will save the time of the filter consumedly. Based on the above methods, the real-time precise clock offset estimation module is added to the PANDA software which developed by the WuHan University for Position and Navigation Data Analysis. In addition, a examples are analyzed for one week observation from 70 IGS global tracking stations around the world with the software, The results show: The software can achieve the 1HZ's updating clock offset resolution for a global network of GPS tracking stations. Also, the accuracy of 0.2ns is achieved with the software compared with the IGS final products.

Periodical orbit maneuver makes a serious problem for navigation users to get correct Geostationary (GEO) orbit information in real-time. As a result, it is very difficult to use GEO satellite for navigation application purpose. In this presentation, the precision of the orbit determination of GEO satellite without maneuver operation has been introduced at first. Then, two strategies of orbit determination during satellite maneuvers are discussed in details. One method is called as maneuver force modeling; the other is empiric force parameter estimation. The results show the residual is of the order of 40 cm by using these strategies, and the position difference between dynamic orbit and kinematics orbit is about 10 m.

In order to implement some kinds of spatial data center system in the grid GIS environment, we need to introduce the concept of grid spatial data center (GSDC). This paper presents the basic architecture of GSDC, which is the research focus of this study. GSDC architecture is based upon and extended from the grid GIS architecture, considers and absorbs the important characters of traditional distributed spatial data center (DSDC). The key techniques and mechanism of DSDC, such as multi-hierarchical spatial data exchange and update, and inhomogeneous data seamless integration, are reconstructed to become the essential services of GSDC in a new form. This paper is also a contribution to achieve an approach to implement such system, and we discuss the various applications of GSDC in appropriate occasions. First of all, this paper analyzes the traditional distributed multi-hierarchical spatial data center architecture in detail, and indicates the senseful ideas and inherent bottleneck problems of it. Then, it gives some advantages come from the developing grid GIS, which can solve the bottleneck problems of DSDC mentioned previously. After presenting the desirability of combining these two powerful things together, this paper analyses the essential services to compose GSDC, which come from the decomposition of the traditional multi-hierarchical DSDC. The original normal modules are transformed into services, and provide their well defined standard function into grid. This paper also gives out the framework of establishing multi-hierarchical virtual organization in GSDC, which is the infrastructure to satisfy the geographical distribution of the using organizations, and the resource sharing and exchange between them. Then, it deals with implementation aspects, and indicates that smooth upgrade from DSDC to GSDC is very important to the application of GSDC. In conclusion, this paper concludes that grid services can give the necessary specifications and standardizations to implement spatial data centers, and can improve the collaboration between them. The disadvantage factors in development of GSDC are also discussed.

With the rapid increase in the amount of spatial data and the number of geospace information system users, current network architecture of geospace information system mainly based on a single server has two primary data transport bottlenecks: the bottleneck of network services throughout provided by the server and the bottleneck of I/O throughout of the storage system for distributed spatial data. In order to avoid both bottlenecks, we present a C/S mode geospace information system based on double-cluster, namely server cluster and storage cluster, and it is called GlobeSIGht. In GlobeSIGht, we use a Linux Virtual Server (LVS) cluster to avoid the bottleneck of network services throughout provided by the server, and use an Object-Based Storage (OBS) cluster to avoid the bottleneck of I/O throughout of the storage system for distributed spatial data. Spatial data is organized as spatial storage object stored in the object-based storage device, and the metadata server manages all the object-based storage devices and spatial storage objects to provide a spatial data shared storage pool as the backend storage of the LVS cluster. Furthermore, by using plug-in technology, GlobeSIGht has integrated many relevant application systems such as 2D geographic information system, 3D geographic information system, multimedia application subsystem, massive MODIS remote sensing images management and dispensing online system.

With the increase of geospatial data and services, how to more efficiently utilize and share the geographic information becomes a crucial problem. To effectively integrate and enhance abundant geographic information anywhere, this paper presents a Registry Centre for Geospatial Web Service (RCGWS) based on the Open Geospatial Consortium (OGC) Catalogue Service for Web and the ebXML Registry Information Model (ebRIM), which provides registration and discovery portals for geospatial metadata for dataset and services. The design ideology and architecture of RCGWS are introduced, and the techniques of appending GIS services classification in extended ebRIM and external interfaces of RCGWS based on OGC CWS are discussed. The implementation of RCGWS platform shows that this Registry Centre can satisfy the requirement of geospatial dataset and services.

The visualization results of multi-dimensional and dynamically spatio-temporal information are diversified by the varying user demands. To realize the sharing of spatio-temporal data, information and knowledge, this paper proposes a technical framework for on-line generation and distribution of the visualization of spatio-temporal information to meet personality need from difference level user; then, describes the key technologies that are involved; final, introduces the implementation and application of the system by taking a marine disaster information system as an example.

Recent advances in computer architectures, computational power and memory bandwidth have created new opportunities for visualizing large terrain data sets. Real 3D terrain simulation results from combining two main data set- an aerial or satellite image as terrain texture and Digital Elevation Model (DEM) as terrain. Because these data are huge amount and in different format, it is necessary to do some prior process for terrain simulation. In this paper a data process chain of RSG DEM and remote sensing image for 3D real terrain simulation is proposed. Then a terrain simulation system using this process method is introduced.

Geographic information portrayal standard within geographic information standards community in ISO 19117 is consisted of symbol specification, symbol assignment, portrayal services, cartographic finishing rules and implementation specification. The portrayal standard specifies a conceptual framework which includes classes, attributes, associations and operations and is the base of implementation and sharing of the geographic information portrayal. The portrayal standard associates geographic features with symbols. This paper starts up with the quantitative methodology for specifying visual variables. Consequently, visual variable values of geographic information portrayal are taken to be statistically analyzed on shape, size and color. Furthermore, a set of quantitative rules of visual variables within the geographic information portrayal system are retrieved. Meanwhile, a visual variable model for portrayal is founded in this paper. At last, the standard definition sharing of the portrayal is implemented with XML, which will become model base of geographic information portrayal and visual analyses for earth observation data.

Aiming at various (near) real-time on-demand services, this paper puts forward the concept of focused geospatial information services with three kinds of service patterns to aggregate and cooperate with all kinds of information services, including the geospatial data services, geospatial processing services, transport network services and sensor services. The challenge of focused geospatial information service is complex in user demand, rich in data dimension, diversified in sensor type, complicated in processing and time-varying in network, which makes intricate in semantics. A hierarchical constraint model is therefore proposed as a uniform semantics description model with four levels including user semantic constraints, data semantic constraints, processing services functional semantic constraint and process service quality semantic constraints. The constraints act role of establishing the connection between user semantics and data services and processing services, and of basic of semantic reasoning in service discovery, selection, and composition.

As remote sensing technology become ever more powerful with multi-platform and multi-sensor, it has been widely recognized for contributing to geospatial information efforts. Because the remotely sensed image processing demands large-scale, collaborative processing and massive storage capabilities to satisfy the increasing demands of various applications, the effect and efficiency of the remotely sensed image processing is far from the user's expectation. The emergence of Service Oriented Architecture (SOA) may make this challenge manageable. It encapsulate all processing function into services and recombine them with service chain. The service composition on demand has become a hot topic. Aiming at the success rate, quality and efficiency of processing service composition for remote sensing application, a remote sensed image processing service composition method is proposed in this paper. It composes services for a user requirement through two steps: 1) dynamically constructs a complete service dependency graph for user requirement on-line; 2) AO* based heuristic searches for optimal valid path in service dependency graph. These services within the service dependency graph are considered relevant to the specific request, instead of overall registered services. The second step, heuristic search is a promising approach for automated planning. Starting with the initial state, AO* uses a heuristic function to select states until the user requirement is reached. Experimental results show that this method has a good performance even the repository has a large number of processing services.

Knowledge sharing and semantic interoperability is a significant research theme in Geographical Information Science (GIScience) because many researchers believe that semantic heterogeneity has been identified as the main obstacle for GIScience development. Interoperability issues can exist at three levels: syntactic, structural (also called systemic) and semantic. The former two, however, can be achieved by implementing international or domain standards proposed by several organizations, for example, Open Geospatial Consortium (OGC), World Wide Web Consortium (W3C) and the International Organization for Standardization/Technical Committee for Geographic information/Geomatics (ISO/TC 211). In this paper, we are concentrating on semantic interoperability, which is the sort of topic that halt conversations and cause people's eyes to glaze over, from two aspects: data/information/knowledge and operation/processing. We presented a service-centered architecture for semantic interoperability of geospatial data and processes. OGC standards like Web Feature Service (WFS) and Web Map Service (WMS) have been employed as normative interfaces for analyzing requests, division requests and delivering small requests. Ontology has been introduced to describe distributed resource including various data and geo-processing operations. The role of interoperability, especially from semantic perspective, has been distinguished at the first section in this paper. As a fundamental principal, the following section introduces semantic web, web service and other related works at this orientation. We present our service-based architecture in detail and its simple application at part three. Conclusion and further orientations have been illustrated at last section.

This paper details a simple knowledge framework, so as to set up a scale-independent spatial database, or so called multi-scaling GIS, for the efficient organization and management of spatial database as well as for multi-scale representation and analysis. GIS is not only the tool for map storage and production but more importantly, GIS is the effective means used for accessing the real word from the map. The framework emphasizes those different thematic feature classes play different roles in the range of different scales and have different effects in the multi-scale representation and processing. Propose multi-grade modeling representation rules based on thematic models in a variety of professional fields, as well as knowledge framework of multiscale representation and processing based on spatial feature classification structure and parameter table.

Now most of distributed virtual geographic environments (DVGE) applications still adopt the centralized pattern, which brings the network congestion or single point of failure to the side of center server. But the P2P technique takes away the bottleneck in data transmission exists in traditional C/S model by virtue of its multilink self-adaptive mechanism of the data transmission, which has a magnitude meaning for researches on the spatial data delivering in distributed virtual geographic environment. As the spatial data has the characteristic of the massive volumes and client change the interesting spatial area in virtual scene so frequently that the spatial application efficiency is sharply decreased, the author brought forward a layered P2P architecture of the spatial data interoperation and flexible group mode in P2P network. A mechanism of layered query of oriented suit (LQOS) and the self-adapted cache mode were introduced to adjust the peer loading and the link numbers for the reliable data capture. In this way, we provide DVGE the rapid data transmission speed among peers, the great data transmission reliability and the better user experience. A DVGE prototype was developed and it proved the efficiency of this P2P DVGE framework. At last the futures of involved techniques and methods are concluded.

Earth observation mission planning for the near space aircrafts is a vital problem in the application of near space aircrafts. It plays an important role in satisfying the requirement of earth observation mission most and increasing the utilization rate of aircraft resources. Considering the mission requirement constrains and payload constrains, the paper conceives the mixed integer programming model for earth observation missions of the near space aircrafts firstly, then analyzes the possible conflicts among the earth observation missions, put forwards the conception of possible conflicts of missions and divides up the whole set of missions into the sets of possible conflicts missions using the conception. The paper gives the calculation method of mission executing conflict degree subsequently and designs a genetic and simulated annealing algorithm based on conflicts resolution. At the end the paper proves the validity of the algorithm by the simulation example.

In order to describe accurately and comprehend quickly the perfect GIS requirements, this article will integrate the ideas of QFD (Quality Function Deployment) and UML (Unified Modeling Language), and analyze the deficiency of prototype development model, and will propose the idea of the Customer-Experienced Rapid Prototyping (CE-RP) and describe in detail the process and framework of the CE-RP, from the angle of the characteristics of Modern-GIS. The CE-RP is mainly composed of Customer Tool-Sets (CTS), Developer Tool-Sets (DTS) and Barrier-Free Semantic Interpreter (BF-SI) and performed by two roles of customer and developer. The main purpose of the CE-RP is to produce the unified and authorized requirements data models between customer and software developer.

A common Sensor Web data service framework for Geo-Processing Workflow (GPW) is presented as part of the NASA Sensor Web project. This framework consists of a data service node, a data processing node, a data presentation node, a Catalogue Service node and BPEL engine. An abstract model designer is used to design the top level GPW model, model instantiation service is used to generate the concrete BPEL, and the BPEL execution engine is adopted. The framework is used to generate several kinds of data: raw data from live sensors, coverage or feature data, geospatial products, or sensor maps. A scenario for an EO-1 Sensor Web data service for fire classification is used to test the feasibility of the proposed framework. The execution time and influences of the service framework are evaluated. The experiments show that this framework can improve the quality of services for sensor data retrieval and processing.

Antarctica plays a key role in many scientific questions, of which those related to global climate change are probably the most prominent examples. There are many researches on Antarctic are carried out at present, and some special institutes sponsored by public and private communities are responsible for antarctica data management and maintenance. Antarctic Spatial Data Infrastructure (AntSDI) [1]sponsored by SCAR's Standing Committee on Antarctic Geographic Information (SC-AGI) is the one responsible for Antarctica spatial data maintenance and sharing by means of OGC standard and specification. Antarctica Spatial Data Infrastructure (AntSDI) has already collected huge volumes of geospatial data and offer an opening geospatial information service. In order to management and use Geospatial data efficiently, and enable most of the users can access to Geospatical data and service at will, we firstly must registry data and service into one or more registry center, then we should construct a building system which can supply users a uniform interface to access data and service in registry center and user also can add their own data and service to system and become part of system's capability. in this paper we present GeoAnt, a prototype interoperable AntSDI building system. GeoAnt is a three-tier standard-based open geospatial web service system which fully automates data discovery, access, and integration steps of the geospatial information discovery process under the interoperable service framework. The paper discusses the system architecture, the individual components of the system and the use of the system in the international project- Grove Mountains GIService Portal (GMGP).

This paper proposes the theory framework of spatial information sharing on digital city, and analyzes its technical characteristics. According to the Service Oriented Architecture (SOA) framework, a geospatial information sharing platform is put forward. The spatial data sharing model based on SOA is designed. A prototype platform of realizing multiple-source spatial information sharing based on ArcGIS Server is developed.

Presently, the spacial social and natural phenomenon is studied by both the GIS technique and statistics methods. However, plenty of complex practical applications restrict these research methods. The data models and technologies exploited are full of special localization. This paper firstly sums up the requirement of spacial statistical analysis. On the base of the requirement, the universal spatial statistical models are transformed into the function tools in statistical GIS system. A pyramidal structure of three layers is brought forward. Therefore, it is feasible to combine the techniques of spacial dada management, searches and visualization in GIS with the methods of processing data in the statistic analysis. It will form an integrative statistical GIS environment with the management, analysis, application and assistant decision-making of spacial statistical information.

MODIS (Moderate Resolution Imaging Spectroradiometer, Moderate Resolution Imaging Spectroradiometer) is carrying on a major satellite remote sensing sensors of EOS series in the United States. MODIS remote sensing data is the new generation of satellite remote sensing information sources; it has broad application prospects in ecological research, environmental monitoring, global climate change and agricultural resources survey and other studies. MODIS data has featured a large volume of data and dealing with complex. In this paper Grid Computing technology brought to the processing of MODIS L1B Data is in order to improve the efficiency. First of all, this paper gives a brief introduction of MODIS L1B data and its application status, also talks about gird computing. Then the structure of MODIS L1B Data Process Based on Grid Computing (MLDPGRID) on logic is given, also explain the function of three tiers. In the realization section, receiving of MODIS L1B data, Grid Platform, software environment and network architecture, processing of MODIS L1B data and portal of MLDPGRID are all discussed. Finally, the paper gives the evaluation and conclusion of the MLDPGRID, meanwhile the optimization strategy and future work are discussed.

The Open Geospatial Consortium, Inc (OGC) Web Services (OWS) were initially primarily simple synchronous Web services based on the HTTP transport protocol, which is perfectly valid in the case of simple geoprocessing of simple data available from local sources. However, with the development of Web-based geospatial technologies, especially the development of the Sensor Web, a number of limitations have been identified with using HTTP-GET/POST binding in OGC OWS, which cannot meet the needs of asynchronous communication and operations between clients and services or in OGC services chain. Asynchronicity in Web services could be achieved in different ways. Callback pattern is widely supported in client asynchronous invocation. Message-based middleware often can be used together with the asynchronous invocation alternatives. Web Notification Service (WNS) is designed to provide asynchronous messagebased communication in OGC. This paper describes a mechanism for an asynchronous, message-based, event-driven, dynamic geospatial Web system based on OGC Web services. The addition of asynchronicity in OGC Web services has two components. One is the augmentation of OGC Web services with asynchronous message-based notification. The other is asynchronous OGC Web service orchestration based on BPEL.

Map decoration is an important component of the topographic map production. To study and tell of the decoration rules in national basic scale topographic maps and multiple-topographic maps, to enhance efficiency, and reduce the workload of topographic map production, the paper plans to study and use of object-oriented technology, to complete the map decoration automatically. The module has been applied to the photographic map and topographic map product development test in the project of 1:50 000 Topographic Mapping of Blank Area in the Western Region, China. The module practice has proved that the realization of Automatic Decoration Technique greatly improves the work efficiency.

The Open Geospatial Consortium (OGC) standard-compliant services define a set of standard interfaces for geospatial Web services to achieve the interoperability in an open distributed computing environment. Grid technology is a distributed computing infrastructure to allow distributed resources sharing and coordinated problem solving. Based on the OGC standards for geospatial services and grid technology, we propose the geospatial grid portal to integrate and interoperate grid-enabled geospatial services. The implementation of the geospatial grid portal is based on a three-tier architecture which consists of grid-enabled geospatial services tier, grid service portal tier and application tier. The OGC standard-compliant services are deployed in a grid environment, the so-called grid-enabled geospatial services. Grid service portals for each type of geospatial services, including WFS, WMS, WCS and CSW, provide a single point of Web entry to discover and access different types of geospatial information. A resource optimization mechanism is incorporated into these service portals to optimize the selection of grid nodes. At the top tier, i.e. the application tier, the client interacts with a semantic middleware for the grid CSW portal, thus allows the semantics-enabled search. The proposed approach can not only optimize the grid resource selection among multiple grid nodes, but also incorporate the power of Semantic Web technology into geospatial grid portal to allow the precise discovery of geospatial data.

How to use web services quickly and efficiently is quite important in geospatial applications. A possible solution of sharing and integrating geospatial resources in opening web environment is to chain distributed and diversified geodata and geoprocessing by using web services. This paper presents an approach for chaining geoprocessing by employing Web Processing Service (WPS) and Business Process Execution Language for Web Services (BPEL4WS) under the service-oriented architecture (SOA) and Open Geospatial Consortium (OGC) standard. Workflow control model and SQL Server based register center are used in a prototype system for chaining geoprocessing web services which have been performed functionality decomposition and packed by using extended WPS.

Spatial service chain composed by multiple spatial services, which distributed on the internet, can realize specific, complicated spatial information processing task. This chain may be involved in bad stability, low usability and low efficiency because of service stateless and variability of service operation environment. In this paper, we abstract the spatial information service to form the Spatial Service Agent (SSA), and attach the spatial information data and processing service to become operation capability of SSA. After introduce contract and policy which are used to restrict and instruct the behavior of the SSA, multi-SSA based spatial information services flow expressed by half-ordered contract set which formed by SSA's negotiation. Through assign specific contract operation rule to each contract and indicate the prerequisite of cooperation, time consuming, and accomplishment state and so on, we can accurately get the service execution state of each step for spatial information services flow monitoring and exception handling.

Spatial information dissemination is a necessary step to make use of spatial information for which is a bridge between the acquirement and the application of spatial information. However, conventional methods of spatial information dissemination cannot cater to users in that not only users' personalized requirements cannot be satisfied, but initiative dissemination service cannot be provided. In this paper, the idea of intelligent spatial information dissemination (ISD) is proposed. Combining the ideas of personalized information retrieval, information filtering and recommender systems, the ISD system employs user profiles and query conditions to provide two implement ways of spatial information disseminations, namely "pull" and "push". Then, the three layers architecture of intelligent spatial information dissemination system based on user profile model is given, and the procedure of data in the ISD system is introduced. Also, the user profile is presented including user profile model and user profile management. Finally, the prototype system of intelligent spatial information dissemination is presented.

Geospatial metadata, data, and services have been widely collected, developed and deployed in recent years. This flourishing of geospatial resources also added to the problem of geospatial heterogeneity. Interoperability research and implementation are needed for advancement in potential solutions to integrate and interoperate these widely dispersed geospatial resources. We design and implement Wuhan Geospatial Information Sharing Platform based on existing WMS. This platform consists of three components: Web Client, Metadata Catalog, and Data Services. Data Services provide WMS. All spatial information from different sources has been published as WMS. These WMS have been described by geospatial metadata compatible with geospatial metadata standard. These geospatial metadata has been stored in Metadata Catalog. Web Client provides functionalities to access and process WMS described by geospatial metadata in Metadata Catalog.

With the rapid development and application of Internet technology, Geographic Information System has stepped into a new age with its main form as Geographic Information Services. Although there are so many Geographic Information Services available on the Internet now, they are still in very low rate of application. To facilitate the discovery, some proposals for Geographic Information Services infrastructures focus on centralized service registry (UDDI, Universal Description, Discovery and Integration ) for cataloguing their geospatial functions and characteristics. Centralized systems introduce single points of failure, hotspots in the network and expose vulnerability to malicious attacks. In order to solve the problem above, this paper proposes A Complex Network Peer-to-Peer Approach for Geospatial Web Services Discovery. Based on complex network theory, a Peer-to-Peer network has been established, and it takes the charge of each peer's communication and management, and an EBRIM registry centre has been inserted into each peer for the registry and query of Geographic Information Services.

Geo-ontology, a kind of domain ontology, is used to make the knowledge, information and data of concerned geographical science in the abstract to form a series of single object or entity with common cognition. These single object or entity can compose a specific system in some certain way and can be disposed on conception and given specific definition at the same time. Ultimately, these above-mentioned worked results can be expressed in some manners of formalization. The main aim of constructing geo-ontology is to get the knowledge of the domain of geography, and provide the commonly approbatory vocabularies in the domain, as well as give the definite definition about these geographical vocabularies and mutual relations between them in the mode of formalization at different hiberarchy. Consequently, the modeling tool of conception model of describing geographic Information System at the hiberarchy of semantic meaning and knowledge can be provided to solve the semantic conception of information exchange in geographical space and make them possess the comparatively possible characters of accuracy, maturity and universality, etc. In fact, some experiments have been made to validate geo-ontology. During the course of studying, Geo-ontology oriented to flood can be described and constructed by making the method based on geo-spatial affairs to serve the governmental departments at all levels to deal with flood. Thereinto, intelligent retrieve and service based on geoontology of disaster are main functions known from the traditional manner by using keywords. For instance, the function of dealing with disaster information based on geo-ontology can be provided when a supposed flood happened in a certain city. The correlative officers can input some words, such as "city name, flood", which have been realized semantic label, to get the information they needed when they browse different websites. The information, including basic geographical information and flood distributing and change about flood with different scales and ranges in the city, can be distilled intellectively and on its own initiative from the geo-ontology database. Besides, correlative statistical information can also be provided to the governmental departments at all levels to help them to carry out timely measures of fighting back disaster and rescue. Compared with the past manners, the efficiency of dealing with flood information has been improved to some extent than ever because plenty of information irrespective and interferential to flood in different websites can be sieved in advance based on the retrieve method oriented to Geo-ontology. In a word, it will take the pursuers long time to study geo-ontology due to actual limited resource. But then, geo-ontology will be sure to further perfect correspondingly especially in the field of Geographic Information System owing to its more and more factual applications.

Large-size of spatial data and limited bandwidth of network make it restricted to transmit spatial data in WebGIS. This paper employs IPv6 (Internet Protocol version 6), the successor of IPv4 running now, to transmit spatial data efficiently. As the core of NGN (Next Generation Network), IPv6 brings us many advantages to resolve performance problems in current IPv4 network applications. Multicast, which is mandatory in IPv6 routers, can make one server serve many clients simultaneously efficiently, thus to improve capacity of network applications. The new type of anycast address in IPv6 will make network client applications possible to find the nearest server. This makes data transmission between client and server fastest. The paper introduces how to apply IPv6 multicast and anycast in WebGIS to transmit data efficiently.

This article discusses the synchronous update mechanism and implementary policy for real-time network of the land survey database based on multi-level distributed heterogeneous structure of "state-province-city-county", and to design and realize the incremental update network system in application to web service model and incremental updating mode of the land survey data. The main procedure of real-time network synchronous update policy in this article includes: data standardization transition, web services network asynchronous transmission, data processing and checking, spatial entity data increment updating. This paper also experiments, tests and validates the model method and updating policy as Laoren village land block data update for an example.

Along with the development of earth observation technology, large amounts of geospatial information are accessible. There are also a lot of geospatial data and services which are shared on the Internet. However they vary in formats and are stored at various organizations leading to problems of data discovery, data interoperability and usability. The Open Geospatial Consortium (OGC) has developed standard service called catalogue in order to overcome this problem. The goal of a geospatial catalogue is to support a wide range of users in discovering relevant geographic data and services from heterogeneous and distributed repositories. But in most of geospatial catalogue services, the search functionality is limited to the direct match of keywords from metadata, the OGC catalogues may not return useful results as the used keywords often do not match with the meta-information stored in the catalogues. In this paper, we propose a geospatial semantic catalogue services that aims at overcoming this limitation.

With the development of space technology, a growing number of the earth observing satellites have been used to acquire data of the earth for various purposes, and the capability of data acquirement from spaceborne sensor makes a rapid enhancement. Under this circumstance, it is very important to measure the capability of data acquirement quantitatively. This paper concerns on the measurement of EOS data acquirement. This measurement can identify access area of a specific EOS. Considering the characteristics of payload, the location of instantaneous imaging area could be calculated based on sensor geometric model. To calculate the location of a given sensor instantaneous imaging area, the measurement is divided into 3 stages: firstly, a satellite motion prediction is undertaken for the purpose of getting position and velocity of the satellite; furthermore, considering the performance of skew maneuver, the imaging area of the satellite's sensor could be calculated based on strict geometric model, finally, the imaging area of sensor is calculated. Experimental results show that the proposed measurement is accurate.

GML can only achieve geospatial interoperation at syntactic level. However, it is necessary to resolve difference of spatial cognition in the first place in most occasions, so ontology was introduced to describe geospatial information and services. But it is obviously difficult and improper to let users to find, match and compose services, especially in some occasions there are complicated business logics. Currently, with the gradual introduction of Semantic Web technology (e.g., OWL, SWRL), the focus of the interoperation of geospatial information has shifted from syntactic level to Semantic and even automatic, intelligent level. In this way, Geospatial Semantic Web (GSM) can be put forward as an augmentation to the Semantic Web that additionally includes geospatial abstractions as well as related reasoning, representation and query mechanisms. To advance the implementation of GSM, we first attempt to construct the mechanism of modeling and formal representation of geospatial knowledge, which are also two mostly foundational phases in knowledge engineering (KE). Our attitude in this paper is quite pragmatical: we argue that geospatial context is a formal model of the discriminate environment characters of geospatial knowledge, and the derivation, understanding and using of geospatial knowledge are located in geospatial context. Therefore, first, we put forward a primitive hierarchy of geospatial knowledge referencing first order logic, formal ontologies, rules and GML. Second, a metamodel of geospatial context is proposed and we use the modeling methods and representation languages of formal ontologies to process geospatial context. Thirdly, we extend Web Process Service (WPS) to be compatible with local DLL for geoprocessing and possess inference capability based on OWL.

This paper firstly introduces the spatial data grid and the CDN (Content Delivery Network) technology. And then it depicts the significance of integrating grid with CDN. On this basis, this paper proposes a method of constructing the spatial data grid system by using CDN to support the massive spatial data online service. Finally, the simulation results by OPNET show that the programme do can improve the system performance, and reduce response time in a greater extent.

This paper presents a P2P approach to the delivery of earth-observation data. Conventional P2P networks can speed up data delivery but suffers the problem of stability when handling spatial data. We design a hybrid CDN-P2P strategy to speed up data delivery while maintaining stability. We tested our strategy in a simulation environment in which performance of data transfer is observed against different bandwidths. The results show that the hybrid approach performs much better than the conventional only-server and only-peers model. Dynamic allocation of peers and predownload are also presented as strategies to address the special needs of spatial data delivery.

This paper proposes a multi-activities scheduling algorithm based extended space-time prism under time geography framework. This algorithm aims to schedule multi-activities from different kinds of activity, which can be used as kernel function of an innovative location based service-multi-activities scheduling service. The extended space-time prism is derived of space-time prism concepts of time geography. For each available activity opportunity in this extended spacetime prism, two new shortest path trees are computed to measure the travel time between activities from different kinds of activity, one new space-time tree arrive at this available activity opportunity, and the another one departs from this available activity opportunity. A prototype 3D space-time environment is implemented to support the concept of extended space-time prism, and provide function of this proposed algorithm. An example of multi-activities scheduling scenario is shown to demonstrate the usefulness of this proposed algorithm for this location based service. Finally, several future works are also introduced in this paper.

Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. Spatial clustering is one of the very important spatial data mining techniques. So far, a lot of spatial clustering algorithms have been proposed. In this paper we propose a robust spatial clustering algorithm named SCABDT (Spatial Clustering Algorithm Based on Delaunay Triangulation). SCABDT demonstrates important advantages over the previous works. First, it discovers even arbitrary shape of cluster distribution. Second, in order to execute SCABDT, we do not need to know any priori nature of distribution. Third, like DBSCAN, Experiments show that SCABDT does not require so much CPU processing time. Finally it handles efficiently outliers.

This paper first synthetically analyzes three classical algorithms of constructing Delaunay Triangulation Irregular Net (DTIN), then uses improved convex hull algorithm to construct non-constrained D-TIN. The non-constrained D-TIN may cause some Delaunay triangles which span contour lines, so these Delaunay triangles must be dealt with. Then the local adjusting algorithm based on diagonals changing of the impacted region is used to embed contour lines into D-TIN as constrained segments and ultimately construct constrained D-TIN (CD-TIN). After constructing CD-TIN which supports the spatial data mining, it is easy to compute normal vectors of Delaunay triangles in the CD-TIN, and then get their gradient values. By means of the relationship of gradient values and terrain features, flats and mountain regions can be distinguished from topographic map based on contour lines. Even the terrain features such as mountain peak can be also recognized in the recognized mountain regions primarily by Flat-Triangle whose three vertexes have the same digital elevation.

We describe a method, based on spatial relations, to generate algorithms for watermarking 2D vector map. The method consists of: defining and computing the metric meatures of topological relations between map objects; extracting cover data from metric meatures; deviding the cover data into different subsets; adjusting the spatial relations between map objects within the precision tolerance of the map to make the cover data distribution of one subset shows one of the two expected patterns to suggest the embedding of bit 1 or 0. The method is blind and experiments show that it is also robust.

Oracle9i database is a very big and complicated run system whose run efficiency is of great importance to E-government system capability. There are large numbers of basic geo-spatial data and attributed data in the E-government system, such as vector data, grid data, image data, DEM data and statistical data, etc. The large oracle9i database and B/S structure with three layers are adopted during the course of system application. It is very important to optimize the database capability due to using oracle9i database to manage the great capacity of data in the system. This paper proposed some optimizational principles and methods of oracle9i database capability in detail in the aspects of database structure, SQL Sentence and memory assignation. At last, some examples are given to validate the above-mentioned principles and methods. In fact, the optimization of oracle9i database is a long and continually dynamic process with the development of time due to great capacity of different data. It involves a lot of work and need often track diversified statistical targets and analyze the cause of capability change. Different aspects of factors must be synthetically considered in order to enhance the run efficiency of the system. During the course of system construction, DBA must analyze carefully different requirements and configure rationally diversified parameters about database structure, SQL sentence and memory assignation so that the run system based on oracle9i database can be at its best and improve the decision-making efficiency of e-government.

In this paper, we specially emphasize on BP Artificial Neural Networks (BP ANNs) in spatial interpolation of DEM, and simulate one spatial interpolation case in the area where there are several discrete known levelling points using input vector: (X, Y, XY, X², Y²) or (X, Y, XY, X², Y², XY², X²Y, X³, Y³)instead of (X,Y), where the X is the horizontal coordinate and the Y is the vertical coordinate. The results show that the new input vectors are usually applicable and better than the classic one. In the numerical experiment of this paper, the maximum error is 2.032m when the input vector, (X, Y, XY, X², Y², XY², X²Y, X³, Y³) is used, while it is 2.807m when (X, Y) is applied. Further this BP ANNs method is better than the classic polynomial method in which the maximum error of polynomial method is 6.728m.

The development and application of spatial database ultimately create the demand for spatial information security, and access control is our primary concern. The access control requirements for spatial database contain two special aspects: 1) fine-grained; 2) meeting certain conditions, including spatial and non-spatial. In this paper, we propose a view-based mechanism to implement access control functionalities. We firstly present the authorization model for spatial data. Then we thoroughly discuss the definition of various views and their authorization, and explain the advantages and disadvantages of this model. We also provide a reference framework for the view-based access control system, and the components and control flow are explained. Finally, we use a case study to demonstrate the feasibility and effectiveness of view-based authorization model for securing spatial database access.

The path planning method is one of the main research directions in current UAV(unmanned aerial vehicle) technologies. In this paper we perform analyses on the adversarial environment which may be broken through during the UAV mission for ground observation, and carry out the grade classification according to the threat level. On the basis of genetic algorithm, the encoding method of dimension reduction and direct quantization is used to combine the threat value of each leg with the flight distance, so as to construct the fitness evaluation function based on the threat amount and design the algorithm. This method is proven to be able to converge effectively and quickly via the simulation experiments, which meet the threat restriction and applicability of UAV in route planning.

Geospatial data are the backbone of spatial analysis, but only current and accurate data can provide the appropriate framework for successful use of GIS technology. The revision of geospatial data is still one of the major open challenges for the successful implementation of Geographic Information Systems (GIS). There is a great need for cost-efficient data revision and quality control methods in order to fulfill the need of most faithful image of the geographic space reality. Maintaining one database per scale without directly maintaining interrelationship between multiple scale databases leads to no update propagation and inter-database consistency is lost. A Multi-resolution/representation-database (MRDB) approach is proposed in this paper to solve this problem. MRDB is a spatial database technology that designed to store one real world phenomena at several specially designed levels of precision, accuracy and resolution. Propagating updates between these different scales datasets in MRDB with the advantage that data consistency and integrity can be significantly improved and enable an automatic incremental update process for the data sets.

This paper gave a brief of basic principles of neural network, analyzed the BP algorithm applications in the neural network, and introduced the BP algorithm machinery. Based on BP algorithm, takes Karst rocky desertification in Du'an country of Guangxi province as example, proposed the neural network Karst rocky desertification early warning model structure which can realize the rocky desertification warning analysis, calculate the rocky desertification fatalness indexes, and expresses the rocky desertification early warning levels in the thematic map. The result verifies the credibility and the possibility of the early warning analysis model with BP algorithm.

Computation Grids enable the coordinated sharing of large-scale distributed heterogeneous computing resources that can be used to solve computationally intensive problems in science, engineering, and commerce. Grid spatial applications are made possible by high-speed networks and a new generation of Grid middleware that resides between networks and traditional GIS applications. The integration of the multi-sources and heterogeneous spatial information and the management of the distributed spatial resources and the sharing and cooperative of the spatial data and Grid services are the key problems to resolve in the development of the Grid GIS. The performance of the spatial index mechanism is the key technology of the Grid GIS and spatial database affects the holistic performance of the GIS in Grid Environments. In order to improve the efficiency of parallel processing of a spatial mass data under the distributed parallel computing grid environment, this paper presents a new grid slot hash parallel spatial index GSHR-Tree structure established in the parallel spatial indexing mechanism. Based on the hash table and dynamic spatial slot, this paper has improved the structure of the classical parallel R tree index. The GSHR-Tree index makes full use of the good qualities of R-Tree and hash data structure. This paper has constructed a new parallel spatial index that can meet the needs of parallel grid computing about the magnanimous spatial data in the distributed network. This arithmetic splits space in to multi-slots by multiplying and reverting and maps these slots to sites in distributed and parallel system. Each sites constructs the spatial objects in its spatial slot into an R tree. On the basis of this tree structure, the index data was distributed among multiple nodes in the grid networks by using large node R-tree method. The unbalance during process can be quickly adjusted by means of a dynamical adjusting algorithm. This tree structure has considered the distributed operation, reduplication operation transfer operation of spatial index in the grid environment. The design of GSHR-Tree has ensured the performance of the load balance in the parallel computation. This tree structure is fit for the parallel process of the spatial information in the distributed network environments. Instead of spatial object's recursive comparison where original R tree has been used, the algorithm builds the spatial index by applying binary code operation in which computer runs more efficiently, and extended dynamic hash code for bit comparison. In GSHR-Tree, a new server is assigned to the network whenever a split of a full node is required. We describe a more flexible allocation protocol which copes with a temporary shortage of storage resources. It uses a distributed balanced binary spatial tree that scales with insertions to potentially any number of storage servers through splits of the overloaded ones. The application manipulates the GSHR-Tree structure from a node in the grid environment. The node addresses the tree through its image that the splits can make outdated. This may generate addressing errors, solved by the forwarding among the servers. In this paper, a spatial index data distribution algorithm that limits the number of servers has been proposed. We improve the storage utilization at the cost of additional messages. The structure of GSHR-Tree is believed that the scheme of this grid spatial index should fit the needs of new applications using endlessly larger sets of spatial data. Our proposal constitutes a flexible storage allocation method for a distributed spatial index. The insertion policy can be tuned dynamically to cope with periods of storage shortage. In such cases storage balancing should be favored for better space utilization, at the price of extra message exchanges between servers. This structure makes a compromise in the updating of the duplicated index and the transformation of the spatial index data. Meeting the needs of the grid computing, GSHRTree has a flexible structure in order to satisfy new needs in the future. The GSHR-Tree provides the R-tree capabilities for large spatial datasets stored over interconnected servers. The analysis, including the experiments, confirmed the efficiency of our design choices. The scheme should fit the needs of new applications of spatial data, using endlessly larger datasets. Using the system response time of the parallel processing of spatial scope query algorithm as the performance evaluation factor, According to the result of the simulated the experiments, GSHR-Tree is performed to prove the reasonable design and the high performance of the indexing structure that the paper presented.

Parallelism of spatial index could significantly improve the performance of spatial queries, special for massive spatial databases, so the research of parallel spatial index takes a important role in high performance spatial databases. Existing parallel spatial index methods have two main shortcoming: one is accessing hotspot and bottleneck of index items located in main server, the other is high costs and complicated operations for maintaining index consistency. Aim at these, a distributed parallel spatial index structure called DPR-tree is proposed. It splits whole index region into partition sub-regions by using Hilbert space-filling curve grid and organizes index sub-regions according to locality of spatial objects, then maps index sub-regions to partition sub-regions and assigns these index sub-regions to different computer nodes by a appointed map function, Each computer node manages a multi-level distributed sub-Rtree which is built from a index sub-region. Our experimental results indicate that the proposed parallel spatial index can achieve speedup well and offer significant potential for reducing query response time.

In some fields of space science such as GPS positioning system and remote sensing image processing, the function model is physically unambiguous and nonstatistical, and many of them are nonlinear. Further more, data collected in many fields of space science include systematic errors inevitably, parametric models sometimes are in difficulty to deal with them, and semiparametric model is an approach to solve this kind of problems. This paper focuses on a kind of semiparametric model with a nonlinear parametric component, in which the nonlinear parametric component is used to express the physical relationship and the nonparametric component is used to describe systematic errors and other model errors. The resolving of nonlinear semiparametric model is a new problem now. The most general method is linearization, but linearization is likely to introduce model error in to the model. In this paper, the direct estimating formulas of kernel method under the least-squares principle of this kind of model are deduced, including the calculating formulae of the estimation of parametric and nonparametric components, and gives the direct nonlinear estimating formulas of kernel estimator considering the second order items. Based on direct estimating formulas and simulated GPS observation data, this paper proved that: as to some least-squares kernel estimating of nonlinear semiparametric models, we can use direct estimating methods considering the second order items.

In data mining one of the classical algorithms is Apriori which has been developed for association rule mining in large transaction database. And it cannot been directly used in spatial association rules mining. The main difference between data mining in relational DB and in spatial DB is that attributes of the neighbors of some object of interest may have an influence on the object and therefore have to be considered as well. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations) which are used by spatial data mining algorithms. Therefore, new techniques are required for effective and efficient spatial data mining. Geostatistics are statistical methods used to describe spatial relationships among sample data and to apply this analysis to the prediction of spatial and temporal phenomena. They are used to explain spatial patterns and to interpolate values at unsampled locations. This paper put forward an improved algorithm of Apriori about mining association rules with geostatistics. First the spatial autocorrelation of the attributes with location were estimated with the geostatistics methods such as kriging and Spatial Autoregressive Model (SAR). Then a spatial autocorrelation model of the attributes were built. Later an improved algorithm of apriori combined with the spatial autocorrelation model were offered to mine the spatial association rules. Last an experiment of the new algorithm were carried out on the hayfever incidence and climate factors in UK. The result shows that the output rules is matched with the references.

Spatial clustering is one of those major methods applying to spatial data mining and knowledge discovery. The purpose of this paper is to set forth Spatial Clustering Method Based on Multidimensional Cloud Model, which can be widely applied to the research on classification and hierarchy in realm of spatial data mining and knowledge discovery. This paper summarizes all kinds of cloud model and analyzes the optimalizing form of spatial data−three-dimensional cloud model. The limitation which sets the weighing value subjectively in traditional way and propagation of error can be avoided. The implementation procedure of this method is advanced, and the feasibility of this method is proven through experiments effectively.

In this paper, a guide star selection algorithm based on angular grids is presented, which can be used to minimize the size of initial star catalogue and guarantee the distribution of the guide stars as uniform as possible . This algorithm is preformed by dividing the FOV into many equal angular grids and mapping the grids onto the celestial sphere. The guide stars are selected in the extent of grids and their brightness and position in the celestial sphere are considered as well. The experiment with real star catalogue data demonstrates the validity of the proposed algorithm.

Prediction of farmland change is a basic work of farmland protection, and also provides basic data for land use planning. According to non-linear characteristic of farmland change, a new method which employs Cellular Automata and Logistic Regression Model to simulate and predict farmland change is discussed in this paper, and structure of Logistic-CA Model and parameters calculation are analyzed. And then, taking Xiantao City as a case, Logistic-CA Model mentioned in this paper was applied to simulate and predict farmland change in this area. Results show: (1)Logistic-CA Model can get rid of disadvantages of traditional mathematic models and get higher accuracy in farmland change prediction; (2)Logistic-CA Model can not only predict quantitative change of farmland, but also simulate pattern evolvement of farmland; (3)Logistic-CA Model can simulate and predict farmland change in various scenarios, and give evidences for establishing policies to protect farmland.

Traditional modeling methods on spatial objects are not eligible to deal well with the fuzzy features that acquired from image, some research need to be carried out on the fuzzy spatial objects modeling. With the deep investigation on the spatial objects model of GIS and the representation of natural geographical feature, fuzzy spatial objects have been proposed by researchers. Referring to the characteristics of the representation of fuzzy spatial objects, a generation method of fuzzy spatial objects based on fuzzy Neural Networks is going to be demonstrated by the authors in this paper. By combining the fuzzy technique and neural networks, utilizing the learning ability to enhance the fuzzy membership function and fuzzy rules, the system will be self-Adaptive. By comparing with the traditional fuzzy objects generation, the method in this paper improves the accuracy of results according to the experiments in this paper.

In order to visualize spatio-temporal data and temporal geographic information in a dynamic way, a spatio-temporal GIS that are capable of demonstrating how geographic phenomena evolving should be established, for the fundamental theory of spatio-temporal GIS, it is urgent to analyze the impetus and mechanism that how spatio-temporal process happened. The authors proposed a set of universal change patterns for modelling of spatio-temporal processes, which builds a fundamental basis for the representation of dynamic phenomena. Change detection algorithms were also developed and pattern association methods were implemented for the modelling of changing geographical world.

Spatio-Temporal Autoregressive Integrated Moving Average (STAIRMA) model family is a very useful tool in modeling space-time series data. It assumes that space-time series data is correlated linearly in space and time. However, in reality most space-time series contains nonlinear space-time autocorrelation structure, which can't be modeled by STARIMA. Artificial neural networks (ANN) have shown great flexibility in modeling and forecasting nonlinear dynamic process. In the paper, we developed an architecture approach to model space-time series data using artificial neural network (ANN). The model is tested with forest fire prediction in Canada. The experimental result demonstrates that STANN achieves much better prediction accuracy than STARIMA model.

With the development of digital city, 3D City Models (3DCM) are created and applied in more and more fields in recent years. However, the geometric structure of 3DCM is complicated and the volume of texture data is huge, which is beyond the real time rendering abilities of current work stations. On the other hand, various applications have different requirements on the accuracy of geometric data and visual effects. Hence, the levels of detail (LoD) of 3DCM is of vital importance for a 3DCM. Because buildings are the main component of a city, research on the LOD of 3DCM focused on the LOD representation of urban buildings by generalization. Different from 2D map generalization, where maps have standardized official scale series, which lead to determinate generalization space and specific levels of detail. However, 3D building group models have no uniform standard to specify the levels of detail, which leads to difficulties in defining specific generalization space and geometric and semantic layer. Aiming at the automatic generalization of 3D urban building group models, this paper proposes a hierarchical partition approach of 3D urban building-group models to obtain different levels of generalization space.

Augmented Reality (AR) is a growing research area in virtual reality and generates a composite view for the user. It is combination of the real scene viewed by the user and a virtual scene generated by the computer that augments the scene with additional information. About 80 percent information in the real world is related with spatial location. The combination of Geographical information system (GIS) and AR technologies would promote the development of outdoor AR systems, and also would explore a new research direction for GIS. The key technologies of outdoor augmented reality GIS, including basic tracking methods, display devices, typical applications and registration processes, are discussed. In indoor augmented reality's closed environments the tracking of position and head orientation as well as the presentation of information is much more unproblematic than the same task in an outdoor environment. The main application task of outdoor augmented reality GIS is the presentation of information to a user while moving through an unknown region. The system helps to detect automatically objects in sight of a person who need its information. It compares the conventional solutions of 3D registration with, while it discusses their algorithm procedure to basic parameters to give out their advantages and disadvantages at different condition. While affine transformation approach uses the idea of computer graphics and vision technology for reference. Its accuracy is mainly based on the precision and speed of scene feature point extracted from natural or artificial feature.

This paper presents a method on how to organize 3D remote sensing images and how to publish these images quickly. We use two levels of grid-based spatial index to organize massive images. First, we divide a huge digital city image into many map sheets (big images). All of map sheets construct a matrix structure. We use row number and column number to encode every map sheet. Second, by using resample and bilinear interpolation method, we build pyramid for every map sheet to form multi-scale hierarchical structure. At the same time building pyramid, we adopt JPEG compression technology to produce JPEG image format files. The number of output image files equals to the number of pyramid layers. Third, divide every pyramid layer image into many small image tiles. The size of each tile image is 256*256 pixels. All of small tiles of each pyramid layer image also construct a matrix structure. We also use row number and column number to encode every small image tile. We create a file directory for each map sheet in order to store all of small image tiles. we neatly combine the spatial index structure with the file name of each tile, which make server be able to return tile to browser side very quickly without any query operation. With the proposed method, we can provide users with a fast and efficiently tool to publish their own spatial information without involving any programming work. The system performance is very good and the response time is almost identical for different size images.

The traditional two-dimensional and three-dimensional GIS can no longer satisfy people's requirement to understand the real world. So, the new three-dimensional digital terrestrial GIS based on digital earth has been developed, which can give users more intuitive information. Further more, in a more realistic manner, the new three-dimensional digital terrestrial GIS can treatment Multi-source space information of models, It can indicate the variety, the quantity and the quality of spatial objects, the spatial location of these objects and the spatial and temporal distribution of the phenomena. This paper is based on GeoGlobe digital terrestrial platform. It requires some fast scheduling data method. Because the data organization and management methods in the traditional 3D GIS are no longer suitable according to the requirement of data scheduling in the digital earth theory, it is necessary to put forward a more reasonable and conformable data structure to implement clipping data scheduling. Advances one method, which takes geometric object model systems to deal with multi-source space model data and takes reasonable data organization, supports quick data scheduling for digital earth and constructs one 3D model database which is fit for digital earth's use.

Airborne LIDAR has a large number of data, which contains a large amount of redundant information. The information have little help or no help on the follow-up analysis, but the redundant data will take up a lot of storage space and computing time, which will take the inconvenience to the LIDAR data transmission and application. Therefore, in this paper we provide a kind of block algorithm on the basis of tile pyramid, which could rapidly process LIDAR data and reduce the data volume in the terrain mapping. Then based on the block algorithm, we put forward the simplification of LIDAR data, the experiment results show that the algorithm can control simplification degree of point cloud, delete the maximum redundant information, adaptive to retain topographical features information. Therefore, we could construct the Multi-Level of Detail Organization of Airborne LIDAR Data, and it can be the basis of the visualization and transmission network of LIDAR data.

Due to finite space, there is an increasing need to plan and develop strategic underground facilities and infrastructures for various military and non-military applications in Singapore in recent years. The awareness of the underground option among planners, developers, and financiers should be increased so that subsurface planning issues can be better addressed. The lack of adequate and accurate 3D spatial data often makes the design and construction of such underground works difficult. It is necessary to integrate all of the spatial objects for underground planning. Over the past two decades, a number of commercial software systems have been developed for 3D geographic and geological modeling. For example, VGEGIS software allows users to create 3D surface geological maps. 3D GeoModeller, a 3D geological modeling and geophysical inversion package, allows project geologists to build realistic 3D geology models. This paper presents an approach to integrate the geographic and geological models for underground planning. A prototype of 3D Geographic Information System (3DGIS) called "3DRock" has been developed by authors to implement the data integration with 3D GeoModeler. The results so far showed that 3DRock is able to integrate the above-surface, surface, and subsurface information available from maps, sections, terrain models, topographic data, drillholes, etc. for the Banyan Basin in Jurong Island, Singapore, in a case study.

The growth of web and wireless of geographical information systems presents a new set of challenges for map personalization services, because web and wireless mobile users of geographical information systems require information that is directly relevant to the specific task in which they are engaged. In this paper, we propose to set up user ontology for map personalization. Based on the user ontology, we put forward and adopt the cartographic generalization for web map personalization visualization driven by user ontology, which remain or reduce online web map description data according to user's interests and increase user's understanding of the web map. Based on this, we set up automatic web cartographic prototype systems to generate web mapping on demand.

Spatial buffer analysis is one of the important and basic spatial operation functions of geographic information system. Studying 2D spatial buffer generation and analysis was more than the 3D spatial presently, thereby a novel 3D buffer generation algorithm based on vector was presented in this paper. 3D spatial solid is represented by B-Rep model and it's buffer is categorized into solid boundary buffer and solid interior, then construct the whole buffer by union operation on them. The boundary features of 3D spatial solid are classified into point feature, line features and face features. Usually, the statistical analysis is necessary after solid buffer generation. Thus, we proposed a new method which can quick judge whether the buffer affecting the surrounding objects based on collision detection theory. Finally, taking the surface and underground 3D space around University City region of Xianlin as the research area, a 3D buffer with complex geological environment and man-made buildings is created based on the new presented algorithm, and spatial statistical analysis is achieved based on the generated 3D buffer. The results show that the improved algorithm is efficient and practicable, especially the proposed 3D buffer generation algorithm based on vector is applicable for convex body and concave body comparing with current approaches.

Mixture spectra are common in remote sensing due to the limitations of spatial resolution and the heterogeneity of land surface. During the past 30 years, a lot of subpixel model have developed to investigate the information within mixture pixels. Linear spectral mixture model (LSMM) is a simper and more general subpixel model. LSMM also known as spectral mixture analysis is a widely used procedure to determine the proportion of endmembers (constituent materials) within a pixel based on the endmembers' spectral characteristics. The unmixing accuracy of LSMM is restricted by variety of factors, but now the research about LSMM is mostly focused on appraisement of nonlinear effect relating to itself and techniques used to select endmembers, unfortunately, the environment conditions of study area which could sway the unmixing-accuracy, such as atmospheric scatting and terrain undulation, are not studied. This paper probes emphatically into the accuracy uncertainty of LSMM resulting from the terrain undulation. ASTER dataset was chosen and the C terrain correction algorithm was applied to it. Based on this, fractional abundances for different cover types were extracted from both pre- and post-C terrain illumination corrected ASTER using LSMM. Simultaneously, the regression analyses and the IKONOS image were introduced to assess the unmixing accuracy. Results showed that terrain undulation could dramatically constrain the application of LSMM in mountain area. Specifically, for vegetation abundances, a improved unmixing accuracy of 17.6% (regression against to NDVI) and 18.6% (regression against to MVI) for R² was achieved respectively by removing terrain undulation. Anyway, this study indicated in a quantitative way that effective removal or minimization of terrain illumination effects was essential for applying LSMM. This paper could also provide a new instance for LSMM applications in mountainous areas. In addition, the methods employed in this study could be effectively used to evaluate different algorithms of terrain undulation correction for further study.

Uncertainty is an integral component in thematic mapping, and descriptors such as percent correctly classified pixels (PCC) and Kappa coefficients of agreement have been devised as thematic accuracy metrics. However, such spatially averaged measures neither offer hints about spatial variation in accuracy, nor are they useful for error propagation in derivatives due to the deficiency that spatial dependency is not properly accommodated. Geostatistics provides a good framework for spatial uncertainty characterization, as conditional simulation is designed for generating equal-probable realizations of often sparsely sampled fields of concern, which can be summarized for error statistics or subjected to particular geo-processing to facilitate error propagation. Often, for modeling errors in area-class maps depicting distributions of spatial classes, stochastic indicator simulation is employed. Unfortunately, indicator approaches suffer from non-invariant behaviors in simulated classes as class labels are drawn from intervals of class probabilities that are arbitrarily ordered. Discriminant space-based models have been proposed to enhance consistency in mapping spatial classes and replicability in modeling spatial categorical uncertainty. This paper explores bivariate (rather than univariate) discriminant models and extends uncertainty modeling from single-time to bi-temporal area-class maps. Experiment using simulated data sets was carried out to quantify errors in area classes and their propagation in change analysis. It was found that there are significant differences between the results obtained by discriminant models and those by indicator geostatistics. Further investigations are anticipated incorporating real data for mapping and propagating errors in area classes.

For the application of high-resolution airborne SAR stereo positioning, a kind of approximately precise parameter error model has been adopted in this paper to discuss the influence of the direct or indirect-inputted parameters' errors on the final stereo positioning accuracy. Through the describing of the precise parameter error model and the analysis about the error sources for the airborne SAR stereo positioning model, the corresponding simulation tests have been taken in the test site, and the results have shown that most of the direct-inputted parameter errors have little influence on the final plane stereo positioning accuracy, while almost all of the them will introduce more error into the elevation computation. And the indirect-inputted phase error arisen from time delay is the main error source during the process of atmosphere transmitting of radar wave.

One quality estimate model was put forward. It combined the common-point comparison with the scale of wavelet analysis. So, the study on uncertainty estimate based on single data set gained enormously advance. The result indicates that when the ratio between the number of common-points and the number of wavelet low-frequency coefficients is 1.5, the model can estimate the quality for multi-scale representation of linear feature. It provided the quality estimate model for multi-scale representation of linear feature based on wavelet analysis.

To use target information for space transformation in remote sensing data field, artificial immune network theory is introduced to multi-spectral remote sensing information mining, based on the knowledge of target spectrum. First, the target spectrums are fuzzy clustered into several subclasses, to retain different features of target in different subclasses. Then we develop a novel Regional-memory-pattern Artificial Immune Idiotypic Network (RAIN) model based on artificial idiotypic network theory, and train RAIN with subclasses samples. And then, the affinities of the target spectrum and other objects can be calculated according to the immune microscopic dynamics including stimulation and suppression effect. Finally, principal component analysis (PCA) is performed to affinities to explore more weak and hidden information. With its application in Baoguto Area, Xinjiang Uyghur Autonomous Region China, choosing tuffaceous siltstone as target object, the result supports the efficiency of the RAIN-affinity-PCA scheme.

Developments of raster data capture technologies and demands from application fields call for advanced raster data analysis methods. Visual data mining that involves human's visual analytical capability in data analysis attracts attention in recent years. Raster datasets usually have large amount of pixels, which may cause serious clotting problem in visualization and thus challenges visual data mining. The research reported here mainly focuses on this problem and tries to construct a hierarchical framework for visual data mining of raster data. In the hierarchical structure, the first level uses volume rendering to visualize the whole raster dataset in attribute space, which can greatly reduce the impact of clotting. To avoid the loss of subtle patterns, the second level makes use of parallel coordinates plot to reflect detailed attribute information. This hierarchical structure ensures that both global and local patterns embedded in data can be detected. In both levels, visualizations of attribute space are linked with that of geographic space. Software prototype was developed and then applied to find small clusters that may relate to possible soil types. Case study result demonstrated the effectiveness of this proposed approach.

This paper proposed a novel method, scaleable moving window (SMW), to mine spatial association rules while fully taking the fact that spatial heterogeneity may widely exist. SMW is based on the traditional association rule mining algorithm, Apriori. However, we integrated different moving windows (in terms of window size and shape) with Apriori. During the spatial association mining process, various sizes of windows were used to move over the region of interest. Each window, after moving through the whole region, will produce a set of association rules within the current location of the moving window. The spatial association pattern was represented by the support value and confidence value of spatial association within all the locations of the moving window by Apriori algorithm. Different windows were tested to compare the effectiveness of the windows. Compared with traditional method where the spatial association was assumed to be for the whole region, the proposed method could well reflect the reality by giving the fact that spatial association spatially varies when spatial heterogeneity exists. This proposed method was applied in the provincial capital city, Wuhan, Hubei province, China where the spatial associations between residential buildings and roads showed spatially varied, which reflected the real condition of the city.

The spatial data in GIS-T database is huge and complicated, discovery knowledge from this database is very important, region traffic network evaluation is one of the important contents. In this paper the author referred to an integrated algorithm combined Ant colony algorithm with FCM to cluster the traffic data of 15 regions of Hubei Province, then used the method of maximizing deviation to arrange the clustering result. From the result we can evaluate the traffic conditions of the 15 regions.

Spatial distribution pattern is an arrangement of two or more spatial objects according to some spatial relations, such as spatial direction, topological and distance relations. In the real world, spatial objects and spatial distribution pattern all vary continuously along the time-line. Traditional spatial and non-spatial data dissevers this continuous spatio-temporal process. Under analyzing relations among spatial object, its attributes and spatial distribution pattern, we brought metaspatio- temporal process, spatio-temporal process and spatial distribution pattern spatio-temporal process. Rainfall in Eastern China has a typical spatial distribution pattern, being composed of the northern rain area and the southern rain area. Through constructing spatio-temporal process transactions, the association rules can be extracted from spatiotemporal process data set by the Apriori algorithm. The result of the spaio-temporal process association rule mining is consistent with the analysis of the theory. Finally, it is concluded that the spatio-temporal process can describe change of a spatial object in a defined time range, and change trend of one entity can be forecasted through varying trend of others based on the valuable spatio-temporal process association rules.

Spatial dependency describes the relationship between one dependent spatial variable and other related spatial variables. This paper constructs two kinds of Fuzzy Neural Networks for spatial dependency mining, the modified fuzzy neural network model and the fuzzy comprehensive assessment network model. The first model is built from general fuzzy neural network model. It has four layers, input layer, fuzzy membership function layer, fuzzy reasoning layer and output layer. The second model is built based on a fuzzy comprehensive assessment algorithm. It has five layers. The first three layers are same as the first model, the fourth and the fifth layer are used to find the maximum membership degree and give the output. We develop the training algorithm for these two models based BP algorithm and genetic algorithm, respectively. This paper adopts a thematic spatial database of land evaluation to test these models. We use experiential knowledge as original rules to build initial FNN models. We can see that original rules (spatial dependencies) are corrected after training. It can be seen that these two models get almost the same revised dependencies, and this indicates that these two models both correct the original ones and get the more objective spatial dependencies. Experiments also indicate these two models are efficient.

A huge amount of geospatial and temporal data have been collected through various networks of environment monitoring stations. For instance, daily precipitation and temperature are observed at hundreds of meteorological stations in Northeastern China. However, these massive raw data from the stations are not fully utilized for meeting the requirements of human decision-making. In nature, the discovery of geographical data mining is the computation of multivariate spatio-temporal correlations through the stages of data mining. In this paper, a procedure of mining association rules in regional climate-changing databases is introduced. The methods of Kriging interpolation, fuzzy cmeans clustering, and Apriori-based logical rules extraction are employed subsequently. Formally, we define geographical spatio-temporal transactions and fuzzy association rules. Innovatively, we make fuzzy data conceptualization by means of fuzzy c-means clustering, and transform fuzzy data items with membership grades into Boolean data items with weights by means ofλ-cut sets. When the algorithm Apriori is executed on Boolean transactions with weights, fuzzy association rules are derived. Fuzzy association rules are more nature than crisp association rules for human cognition about the reality.