This PDF file contains the front matter associated with SPIE Proceedings Volume 9263, including the Title Page, Copyright information, Table of Contents, Authors, Introduction (if any), and Conference Committee listing.

The performance of environmental monitoring heavily depends on the availability of consecutive observation data and it turns out an increasing demand in remote sensing community for satellite image data in the sufficient resolution with respect to both spatial and temporal requirements, which appear to be conflictive and hard to tune tradeoffs. Multiple constellations could be a solution if without concerning cost, and thus it is so far interesting but very challenging to develop a method which can simultaneously improve both spatial and temporal details. There are some research efforts to deal with the problem from various aspects, a type of approaches is to enhance the spatial resolution using techniques of super resolution, pan-sharpen etc. which can produce good visual effects, but mostly cannot preserve spectral signatures and result in losing analytical value. Another type is to fill temporal frequency gaps by adopting time interpolation, which actually doesn't increase informative context at all. In this paper we presented a novel method to generate satellite images in higher spatial and temporal details, which further enables satellite image time series simulation. Our method starts with a pair of high-low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and the temporal change is then projected onto high resolution data plane and assigned to each high resolution pixel referring the predefined temporal change patterns of each type of ground objects to generate a simulated high resolution data. A preliminary experiment shows that our method can simulate a high resolution data with a good accuracy. We consider the contribution of our method is to enable timely monitoring of temporal changes through analysis of low resolution images time series only, and usage of costly high resolution data can be reduced as much as possible, and it presents an efficient solution with great cost performance to build up an economically operational monitoring service for environment, agriculture, forest, land use investigation, and other applications.

The aim of data conflation is to synergise geospatial information from different sources into a common framework, which can be realised using multivariate geostatistics. Recently, multiple-point geostatistics (MPG) has been proposed for data conflation. Instead of the variogram, MPG borrows structures from the training image, so the spatial correlation is characterised by multiple-point statistics. In pattern-based MPG, two sets of data can be integrated by utilising the secondary data as a locally varying mean (LVM). The training image provides a spatial correlation model and is incorporated to facilitate reproduction of similar local patterns in the predicted image. However, the current patternbased MPG gathers similar patterns based on a prototype class, which extracts spatial structures in an arbitrary way. In this paper, we proposed an improved pattern-based MPG for conflation of digital elevation models (DEMs). In this approach, a new strategy for forming prototype class is applied, which is based on the residual surface, vector ruggedness measure (VRM) and ridge valley class (RVC) of terrain data. The method was tested on the SRTM and GMTED2010 data. SRTM data at the spatial resolution of 3 arc-second was simulated by conflating sparse elevation point data and GMTED2010 data at a coarser spatial resolution of 7.5 arc-second. The proposed MPG method was compared with the traditional pattern-based MPG simulation. Several kriging predictors were applied to provide LVMs for MPG simulation. The result shows that the new method can achieve more precise prediction and retain more spatial details than the benchmarks.

New imaging mode has been brought up for collecting multiple scenes in one pass, as is implemented on World View-II. This greatly helps for acquiring high spatial resolution images that cover urban areas, and is to be adopted in the coming Chinese satellites. This paper is to discuss the mosaic characteristic and propose a mosaic line generation method by integrating correlation and the road information. The mosaic line is formed by linking the unique mosaic point on each line restricted within the road. We position the starting point by connectivity analysis of the road lines, and then locate the adjacent point along the road with connectivity analysis. A weighed vector, combining correlation and distance to centre of the road, is used to pick the best point. The points are located on the road unless it is unavoidable, for example, the road ends or the line touches edge of the image. This method provides instant mosaic line generation for urban areas with road information available in most cases. By resorting to the road, the mosaic line is more applicable since many problems for mosaic of high spatial resolution images are solved, for example, tilting of the buildings, the shadows, motions of the vehicles etc. Experiments have been done with WV-II images and gained favorable results.

Image segmentation is the basis of object-based information extraction from remote sensing imagery. Image segmentation based on multiple features, multi-scale, and spatial context is one current research focus. The scale parameters selected in the segmentation severely impact on the average size of segments obtained by multi-scale segmentation method, such as the Fractal Network Evolution Approach (FNEA) employed in the eCognition software. It is important for the FNEA method to select an appropriate scale parameter that causes no neither over- nor undersegmentation. A method for scale parameter selection and segments refinement is proposed in this paper by modifying a method proposed by Johnson. In a test on two images, the segmentation maps obtained using the proposed method contain less under-segmentation and over-segmentation than that generated by the Johnson’s method. It was demonstrated that the proposed method is effective in scale parameter selection and segment refinement for multi-scale segmentation algorithms, such as the FNEA method.

Information extracted from high resolution satellite images, such as roads, buildings, water and vegetation, has a wide range of applications in disaster assessment and environmental monitoring. At present, object oriented supervised learning is usually used in the objects identification from the high spatial resolution satellite images. In classical ways, we have to label some regions of interests from every image to be classified at first, which is labor intensive. In this paper, we build a feature base for information extraction in order to reduce the labeling efforts. The features stored are regulated and labeled. The labeled samples for a new coming image can be selected from the feature base. And the experiments are taken on GF-1 and ZY-3 images. The results show the feasibility of the feature base for image interpretation.

Band selection provides performance improvement in hyperspectral applications such as target detection, spectral unmixing and classification. Signal-to-noise ratio estimation (SNRe) as a method can be adjusted for different specific applications. SNRe is usually used to remove some low SNR bands from original hyperspectral data in a preprocessing stage and then other band selection methods are applied for the remaining high SNR bands of hyperspectral data to make the operations more efficient. In this paper, we take advantage of SNRe to select the bands which contain the largest amount of information. The wavelet transform is first used to realize the signal-noise separation and get the noise standard deviation of each band, and then the SNRs of all bands are calculated orderly. Considering some time-consuming operations in SNRe algorithm which can’t satisfy some real time applications are very suitable for high performance computing(HPC) in parallel, we design a new massively parallel algorithm to accelerate the SNR estimation algorithm on graphics processing units(GPUs) using the compute device unified architecture(CUDA) language. In addition the implementation of our GPU-based SNRe algorithm has extremely explored the possible parallelism in the C code and been debugged carefully to verify its correctness and efficiency. Experiments are conducted on two sets of real hyperspectral images and considerable acceleration is obtained.

To analyze the composition of lunar surface minerals, one of the scientific payloads of the Chang’E 3 Yutu rover, the Visible and Near-infrared Imaging Spectrometer (VNIS), was developed to detect lunar surface objects and to obtain their reflectance spectra and geometric images. The VNIS, which uses acousto-optic tunable filters as dispersive components, consists of a VIS/NIR imaging spectrometer (0.45-0.95 μm), a shortwave IR spectrometer (0.9-2.4 μm), and a calibration unit with dust-proofing functionality. It is capable of synchronously acquiring the full spectra of lunar surface objects and performing in-situ calibration. After landing successfully on the Moon, the VNIS performed several explorations and calibrations, and obtained several spectral images and spectral reflectance curves of the lunar soil in the Imbrium region. This paper introduces the VNIS, including its working principle, implementation, operation, and major specifications, as well as the initial scientific achievement of lunar surface exploration.

As an optical remote sensing equipment, the thermal infrared hyperspectral imager operates in the thermal infrared spectral band and acquires about 180 wavebands in range of 8.0~12.5μm. The field of view of this imager is 13° and the spatial resolution is better than 1mrad. Its noise equivalent temperature difference (NETD) is less than 0.2K@300K(average). 1 The influence of background radiation of the thermal infrared hyperspectral imager,and a simulation model of simplified background radiation is builded. 2 The design and implementationof the Cryogenic Optics. 3 Thermal infrared focal plane array (FPA) and special dewar component for the thermal infrared hyperspectral imager. 4 Parts of test results of the thermal infrared hyperspectral imager.The hyperspectral imaging system is China’s first success in developing this type of instrument, whose flight validation experiments have already been embarked on. The thermal infrared hyperspectral data acquired will play an important role in fields such as geological exploration and air pollutant identification.

We present the design of a compact, wide-angle pushbroom hyperspectral imager with a 42-deg wide field of view and a broadband response that covers the spectral range 450 to 2500 nm and provides a spectral sampling of 10 nm on SWIR and 5nm on VISNIR. The hyperspectral imager has finished the remote sensing experiment by emplaning on the YUN-12,and its performance meets the design parameters. The design is the high level technology and serves as an example for illustrating the design principles specific to this type of system.

For years, scientists have used thermal broadband cameras to perform target characterization in the longwave (LWIR) and midwave (MWIR) infrared spectral bands. The analysis of broadband imaging sequences typically provides energy, morphological and/or spatiotemporal information. However, there is very little information about the chemical nature of the investigated targets when using such systems due to the lack of spectral content in the images. In order to improve the outcomes of these studies, Telops has developed dynamic multispectral imaging systems which allow synchronized acquisition on 8 channels, at a high frame rate, using a motorized filter wheel. An overview of the technology is presented in this work as well as results from measurements of solvent vapors and minerals. Time-resolved multispectral imaging carried out with the Telops system illustrates the benefits of spectral information obtained at a high frame rate when facing situations involving dynamic events such as gas cloud dispersion. Comparison of the results obtained using the information from the different acquisition channels with the corresponding broadband infrared images illustrates the selectivity enabled by multispectral imaging for characterization of gas and solid targets.

Different objects require different imaging spectra, and a high-temporal-resolution imaging spectrometer has further requirements for producing a large field of view(FOV). For these reasons, we present a new system based on a staring/scanning area-array detector to allow for wide-FOV imaging, along with an acousto-optical tunable filter (AOTF) to impart quick, programmable selection of spectra. Using AOTF as a dispersive component and a turntable as the motion platform, we designed a staring, spectral-programmable imaging prototype with a working band of 400~1000 nm. Besides, we could obtain a large FOV by image mosaic. We performed imaging experiments and image processing for an initial verification of this technique. This technical solution can be used not only for VNIR, but also in SWIR, MWIR, and other bands; it adapts to spectral and FOV requirements for varying subjects, and provides an advanced technical means for further application of high-resolution spectral imaging.

Cyanobacterial harmful algal blooms (CHABs) is a major water quality issue in surface water bodies because of its scum and bad odor forming and toxin producing abilities. Terminations of blooms also cause oxygen depletion leading to hypoxia and widespread fish kills. Therefore, continuous monitoring of CHABs in recreational water bodies and surface drinking water sources is highly required for their early detection and subsequent issuance of a health warning and reducing the economic loss. We present a comparative study between a modified quasi-analytical algorithm (QAA) and a novel three-band algorithm (PC3) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland waters. An extensive dataset, consisting of radiometric measurements, absorption measurements of phytoplankton, organic matter, detritus, and pigment concentration, was used to optimize the algorithms. The QAA algorithm isolates the PC signal from the remote sensing reflectance data using a set of radiative transfer equations and retrieves PC concentration in the water bodies through bio-optical inversion. Validation of the QAA algorithm, using an independent dataset, produced a mean relative error (MRE) of 34%. For the PC₃ algorithm, we propose a coefficient (ψ) for isolating the PC absorption component at 620 nm. Results show that inclusion of the model coefficient relating chlorophyll-a (chla) absorption at 620 nm to 665 nm enables PC₃ to compensate for the confounding effect of chl-a and considerably increases the accuracy of the PC prediction algorithm. The MRE of prediction for PC₃ was 27%. Moreover, PC₃ eliminates the nonlinear sensitivity issue of PC algorithms at high range.

The demand for iron ore has increased in the recent years, thereby necessitating the adoption of rapid and accurate approaches to iron ore exploration and its grade-assessment. It is in this context that hyperspectral radiometry is seen as a potential tool. This paper examines the potential of hyperspectral radiometry in the visible, NIR and SWIR regions of the EMR to assess the grades of hematite of the western Singhbhum iron ore belt of eastern India, in a rapid manner. Certain spectro-radiometric measurements and geochemical analysis were carried out and the results have been presented. From the spectral measurements, it is seen that the strength of reflectance and absorption at definite wavelength regions is controlled by the chemical composition of the iron ores. It is observed that the primary spectral characteristics of these hematite lie in the 650-750nm, 850 to 900nm and 2130-2230nm regions. The laboratory based hyperspectral signatures and multiple regression analysis of spectral parameters and geochemical parameters (Fe₂O₃% and Al₂O₃%) predicted the concentration of iron and alumina content in the hematite. A very strong correlation (R²=0.96) between the spectral parameters and Fe% in the hematite with a minimum error of 0.1%, maximum error of 7.4% and average error of 2.6% is observed. Similarly, a very strong correlation (R²=0.94) between the spectral parameters and Al₂O₃% in the iron ores with a minimum error of 0.04%, maximum error of 7.49% and average error of 2.5% is observed. This error is perhaps due to the presence of other components (SiO₂, TiO₂, P₂O etc.) in the samples which can alter the degree of reflectance and hence the spectral parameters. Neural network based multi-layer perception (MLP) analysis of various spectral parameters and geochemical parameters helped to understand the relative importance of the spectral parameters for predictive models. The strong correlations (Iron: R²=0.96; Alumina: R²=0.94) indicate that the laboratory hyperspectral signatures in the visible, NIR and SWIR regions can give a better estimate of the grades of hematite in a rapid manner.

According to the merits of remotely sensed data in depicting regional land cover and Land changes, multi- objective information processing is employed to remote sensing images to analyze and simulate land cover in mining areas. In this paper, multi-temporal remotely sensed data were selected to monitor the pattern, distri- bution and trend of LUCC and predict its impacts on ecological environment and human settlement in mining area. The monitor, analysis and simulation of LUCC in this coal mining areas are divided into five steps. The are information integration of optical and SAR data, LULC types extraction with SVM classifier, LULC trends simulation with CA Markov model, landscape temporal changes monitoring and analysis with confusion matrixes and landscape indices. The results demonstrate that the improved data fusion algorithm could make full use of information extracted from optical and SAR data; SVM classifier has an efficient and stable ability to obtain land cover maps, which could provide a good basis for both land cover change analysis and trend simulation; CA Markov model is able to predict LULC trends with good performance, and it is an effective way to integrate remotely sensed data with spatial-temporal model for analysis of land use / cover change and corresponding environmental impacts in mining area. Confusion matrixes are combined with landscape indices to evaluation and analysis show that, there was a sustained downward trend in agricultural land and bare land, but a continues growth trend tendency in water body, forest and other lands, and building area showing a wave like change, first increased and then decreased; mining landscape has undergone a from small to large and large to small process of fragmentation, agricultural land is the strongest influenced landscape type in this area, and human activities are the primary cause, so the problem should be pay more attentions by government and other organizations.

This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and without the four new spectral bands. Classification accuracy assessment results show that object-based classification with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%) method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.

The NAST-I and SHIS ultra spectral interferometer sounders flew on the NASA ER-2 aircraft during the May 2013 S-NPP Calibration/Validation Campaign. The ER-2 under flew the Metop-A and –B, Aqua, and SNPP satellites, which carry the IASI, AIRS, and CrIS ultra spectral sounding instruments, respectively. Special ground truth radiosonde and surface based upward viewing ultraspectral radiance Planetary boundary Layer (PBL) sounding observations (i.e., from the AERI and the ASSIST interferometer spectrometers) were obtained at the DOE Southern Great Plains (SGP) ARM CART-site and from a mobile ground site located in Yuma, Arizona. A common physical/statistical sounding retrieval algorithm and statistical database have been applied to the aircraft, ground-based interferometer, and satellite ultra spectral radiance data in order to use the higher spatial resolution aircraft data and higher vertical resolution surface-based interferometer PBL soundings, and radiosonde profiles, to validate the satellite sounding products. Differences between the satellite and the surface/airborne ground “truth” measurements are discussed. In particular, the comparisons between the satellite retrieved profiles and the ground truth observations revealed that improvements in the specification of surface emissivity spectra were needed in order to retrieve accurate atmospheric structure in the Planetary Boundary Layer (PBL). As a result a physical simultaneous surface skin/surface emissivity determination algorithm was implemented which improved the accuracy of atmospheric profiles retrieved throughput the lower trophosphere. Here, special emphasis is given to validating the satellite atmospheric stability and time tendency observations made prior to the development of the devastating Moore, OK tornado on May 20, 2013.

Experimental field campaigns, including satellite under-flights with well-calibrated FTS sensors aboard high-altitude aircraft, are an essential part of the satellite measurement system validation task aimed at improving observations of the Earth’s atmosphere, clouds, and surface for enabling enhancements in weather prediction, climate monitoring capability, and environmental change detection. The Suomi NPP (SNPP) airborne field campaign was conducted during the 6 - 31 May, 2013 timeframe based out of Palmdale, CA, and focused on under-flights of the SNPP satellite with the NASA ER-2 aircraft in order to perform cal/val of the satellite instruments and their corresponding data products. Aircraft flight profiles were designed to under-fly multiple satellites within a single sortie, when feasible, to address satellite sensor validation and cross-validation; specifically, in addition to under-flying SNPP, flight profiles were defined to also obtain data coincident with the NASA A-train (i.e. AQUA), MetOP-A, and MetOP-B satellites to enable intercomparisons with instruments aboard those platforms (i.e. AIRS, IASI, and CrIS). This presentation focuses on radiance analysis from the SNPP airborne field campaign with a particular emphasis on NAST-I intercomparisons with the Crosstrack Infrared Sounder (CrIS).

Characterization of gas clouds are challenging situations to address due to the large and uneven distribution of these fast moving entities. Whether gas characterization is carried out for gas leaks surveys or environmental monitoring purposes, explosives and/or toxic chemicals are often involved. In such situations, airborne measurements present distinct advantages over ground based-techniques since large areas can be covered efficiently from a safe distance. In order to illustrate the potential of airborne thermal infrared hyperspectral imaging for gas cloud characterization, measurements were carried out above smokestacks and a ground-based gas release experiment. Quantitative airborne chemical images of carbon monoxide (CO) and ethylene (C₂H₄) were obtained from measurements carried out using a midwave (MWIR, 3-5 μm) and a longwave (LWIR, 8-12 μm) airborne infrared hyperspectral sensor respectively. Scattering effects were observed in the MWIR experiments on smokestacks as a result of water condensation upon rapid cool down of the hot emission gases. Airborne measurements were carried out using both mapping and targeting acquisition modes. The later provides unique time-dependent information such as the gas cloud direction and velocity.

Spectrally resolved infrared (IR) and far infrared (FIR) radiances measured from orbit with extremely high absolute accuracy are a critical observation for future climate benchmark missions. For the infrared radiance spectra, it has been determined that a measurement accuracy, expressed as an equivalent brightness temperature error, of 0.1 K (k = 3) confirmed on orbit is required for signal detection above natural variability for decadal climate signatures [1, 2]. The challenge in the sensor development for a climate benchmark measurement mission is to achieve ultra-high accuracy with a design that can be flight qualified, has long design life, and is reasonably small, simple, and affordable. The required simplicity is achievable due to the large differences in the sampling and noise requirements for the benchmark climate measurement from those of the typical remote sensing infrared sounders for weather research or operational weather prediction. The University of Wisconsin Space Science and Engineering Center, with funding from the NASA Instrument Incubator Program (IIP), developed the Absolute Radiance Interferometer (ARI), which is designed to meet the uncertainty requirements needed to establish spectrally resolved thermal infrared climate benchmark measurements from space. The ARI is a prototype instrument designed to have a short upgrade path to a spaceflight instrument. Recent vacuum testing of the ARI, conducted under funding from the NASA Earth Science Technology Office, has demonstrated the capability to meet the 0.1 K (k = 3) uncertainty requirement on-orbit. An overview of the instrument design and summary of the radiometric performance verification of the UW-SSEC ARI will be presented.

Future NASA infrared remote sensing missions will require better absolute measurement accuracies than now available, and will most certainly rely on the emerging capability to fly SI traceable standards that provide irrefutable absolute measurement accuracy. To establish a CLARRREO-type climate benchmark, instrumentation will need to measure spectrally resolved infrared radiances with an absolute brightness temperature error of better than 0.1 K, verified onorbit. This will require an independent high-emissivity (<0.999) verification blackbody with an emissivity uncertainty of better than 0.06%, an absolute temperature uncertainty of better than 0.045K (3 sigma), and the capability of operation over a wide range of (Earth scene) temperatures. Key elements of an On-Orbit Absolute Radiance Standard (OARS) meeting these stringent requirements have been demonstrated in the laboratory at the University of Wisconsin and have undergone further refinement under funding from NASA’s Earth Science and Technology Office, culminating in an end-to-end demonstration under vacuum with a prototype climate benchmark instrument. We present the new technologies that underlie the OARS, and the results of testing that demonstrate the required accuracy is being met in a vacuum environment. The underlying technologies include: on-orbit absolute temperature calibration using the transient melt signatures of small quantities (<1g) of reference materials (gallium, water, and mercury) imbedded in the blackbody cavity; and on-orbit cavity spectral emissivity measurement using a carefully baffled heated halo placed in front of the OARS blackbody viewed by the infrared spectrometer system. Emissivity is calculated from the radiance measured from the blackbody combined with the knowledge of key temperatures and radiometric view factors.

Visible and Near-infrared Imaging Spectrometer (VNIS) is one of the scientific payloads mounted on “Yutu” rover in Chang’e 3 lunar exploration project. The VNIS is composed with a visible and near-infrared (0.45-0.95 μm) spectral imager and a short waveband (0.9-2.4 μm) spectrometer on basis of Acousto-Optic Tunable Filter. According to the in-situ analysis, a calibration unit was also equipped for high precisely spectral radiance and reflectance inversion by using solar as standard calibration source. The calibration unit was driven by lightweight ultrasonic motor, and it could be located on three fixed position including detection (full-opened), calibration (horizontal) and dust-proof (closed). In this paper, the principle of VNIS, especially calibration unit was described firstly. Then, radiometric correction algorithms on lunar surface based on standard solar spectral irradiance were expounded. Through the analysis of VNIS scientific data, the spectral radiance and reflectance curves of detection area were shown in the end.

The GOSAT thermal infrared (TIR) band calibration is reviewed for the five-year time period from April 2009 through March 2014. Pre-launch characterization has been augmented by post-launch analysis of on-orbit data and comparison with coincident measurements from other satellite, airborne, and ground-based sensors. Successive refinements of the TIR band ground-processing software have incorporated corrections for detector non-linearity and polarization. Estimates of radiometric uncertainty have also been made. The comparison of GOSAT TIR band nadir and off-nadir comparisons (SNOs and SONOs) provide a quantitative spectral assessment of the radiometric bias relative to the NASA AIRS and EUMETSAT IASI sensors.

Mangrove ecosystem study is one of the main beneficiaries of the application of hyperspectral data and spectral matching techniques. Diversity and density of mangrove species leads to complexity of the ecosystem. Hence, species level mapping becomes difficult. Though hyperspectral images are appropriate for such a mapping, different mangrove species with closely matching spectra pose a challenge. This paper proposes a novel hyperspectral matching algorithm by integrating the stochastic Jeffries-Matusita measure (JM) and deterministic Spectral Angle Mapper (SAM) to accurately map most species of the mangrove ecosystem. The JM-SAM algorithm signifies the combination of an quantitative angle measure (SAM) and an qualitative distance measure (JM). The spectral capabilities of both the measures are orthogonally projected using tangent and sine functions to result in the combined algorithm. The developed JM-SAM algorithm is implemented to discriminate the mangrove species and the landcover classes of Pichavaram and Muthupet mangrove forests of southern India using the Hyperion datasets. The developed algorithm is extended in a supervised framework for improved classification of the Hyperion image. The reference spectra of the mangrove species and other cover types are extracted from the Hyperion image. From the values of relative spectral discriminatory probability and relative discriminatory entropy value, it can be inferred that hybrid JM-SAM matching measure results in improved discriminability than the individual SAM and JM algorithms. This performance is reflected in the classification results where the JM-SAM (TAN) and JM-SAM (SIN) matching algorithms yielded an improved accuracy of (86.25%,85%) and (88.10%, 86.96) for both the study sites.

Light use efficiency (LUE ) is an important parameter for GPP and and NPP estimation model, cause by the existing model method to estimate the actual LUE is always simple and rough, which may lead to serious bias by GPP and NPP. The photochemical reflectance index ( PRI ) has great potential for direct estimation the actual LUE. In this paper, wheat in different nitrogen treatments was designed in field trial during Wheat growing period, for obtain photosynthesis and reflective hyperspectral data, and then LUE and PRI was calculated in critical period of wheat growth. The results show that, at different growth stages under three different nitrogen conditions, LUE and PRI value were significantly increased with increasing nitrogen absorption; Last longer, more capable of absorbing nitrogen amount, the correlation between LUE and PRI was better，for example, the correlation coefficient is obviously larger in heading stage than elongation stage for same nitrogen treatment.

LAI is a crucial parameter and a basic quantity indicating crop growth situation. Empirical models comprising spectral indices (SIs) and LAI have widely been applied to the retrieval of LAI. SI method already has exhibited feasibility in the estimation of vegetation LAI. However, it is largely subject to the inconsistency from different remote sensors which have varied specifications, such as spectral response features and central wavelength. To address this issue, a new vegetation index (VIUPD) based on the universal pattern decomposition method was proposed. It is expressed as a linear sum of the pattern decomposition coefficients and features in sensor-independency. The aim of this study was to evaluate the prediction accuracy and stability of VIUPD for estimating LAI, compared with three other common-used SIs. In this study, the measured spectra were resampled to simulated TM multispectral data and Hyperion hyperspectral data respectively, using the Gaussian spectral response function. The three typical SIs chosen were including NDVI, TVI and MCARI, which were constructed with the sensitive bands to the LAI. Finally, the regression equations between four selected SIs and LAI were established. The best index evaluated using the simulated TM data was VIUPD which exhibits the best correlation with LAI (R²=0.92) followed by NDVI (R²=0.80). For the simulated Hyperion data, VIUPD again ranks first with R²=0.89, followed by TVI (R2=0.63). Meanwhile, the consistence of VIUPD also was studied based on simulated TM and Hyperion sensor data and the R² reached to 0.95. It is demonstrated that VIUPD has the best accuracy and stability to estimate LAI of winter wheat whether using simulated TM data or Hyperion data, which reaffirms that VIUPD is comparatively sensor independent.

Urban grass is the interference object of vegetable species recognition. Therefore choose an instance of urban grass to retrieve the spectrum curve of interference vegetation. The spectrum retrieval of vegetation species includes three steps, 1) the Hyperspectral image preprocessing, 2) the high fidelity image fusion, and 3) the purity endmember extraction. Firstly, the Hyperspectral image is preprocessed including the removal of bad bands, the radiance calibration, and the FLAASH atmospheric correction. Secondly, the Gram-Schmidt fusion method which has an advantage of spectral high fidelity was employed to fuse the Hyperspectral image and the high spatial panchromatic image. Thirdly, the grass reference vectors was applied in masking the fusion image and then the minimum noise fraction was used to forward and inverse transform the masking image. The pixel purity index of image was calculated after de-noising and then the threshold range was determined to obtain the region of interest that has high purity. The principal component analysis was adopted to forward transform the visible, near infrared, shortwave infrared channels respectively and then the first and second bands of each channel were selected. The optimum index factor was used to acquire the eigenvalues of optimum bands combination and then the N-dimensional visualization was applied in extracting study area endmember of grass species. Finally the spectrum curve of urban grass was retrieved from the average endmember spectral of original fusion image.

Global warming has become a very serious issue for human beings. The substantial increase of column carbon dioxide (CO2) results in temperature raised of the earth’s surface. One important specification is that it must have an ultra-spectral ability to measure concentration inversion of CO2, developing ultra-spectral remote sensors is an significant direction. This paper brings a new spectrometer on atmospheric sounding, that splits spectrum with a new type of narrow-band interference filter. It can simultaneity get super finely spectrum, compact configuration, and easy to achieve. That has broad applied foreground.

In this paper, a novel local ways to implement hyperspectral anomaly detector is presented. Usually, the local detectors are implemented in the spatial window of image scene, but the proposed approach is implemented on the windows of spectral space. As a multivariate data, the hyperspectral image datasets can be considered as a low-dimensional manifold embedded in the high-dimensional spectral space. In real environments, nonlinear spectral mixture occurs more frequently. At these situations, whole dataset would be distributed in one or more nonlinear manifolds in high dimensional space, such as a hyper-curve surface or nonlinear hyper-simplex. However, the majority of global and local detectors in hyperspectral image are based on the linear projections. They are established on the assumption that the geometric distribution of datasets is a linear manifold. It is incapable for them to deal with these nonlinear manifold data, even for spatial local data. In this paper, a novel anomaly detection algorithm based on local linear manifold is put forward to handle the nonlinear manifold problems. In the algorithm, the local neighborhood relationships are established in spectral space, and then an anomaly detector based on linear projection is carried out in these local areas. This situation is similar to using sliding windows in the spectral space. The results are compared with classic spatial local algorithm by using real hyperspectral image and demonstrate the effectiveness in improving the weak anomalies detection and decreasing the false alarms.

In this study we introduce brief description and the main approaches used in system development. System is devising with a participation of Pacific Oceanological Institute (FEB RAS), Institute of Automation and Control Processes (FEB RAS) and also Maritime State University, n.a. G.I. Nevelskoy. For many years research team of these institutions carried out a lot of field measurements and collected a lot of remote sensing data, using spectrophotometers, LIDARs, fluorometers. The primary goal of this development – bring all this data together to integrated database and design user-friendly interface to work with. “InSitu-Eye” will perform standard routine operations, such as sampling data according to certain parameters; gridding and timing of data; filtering and quality check of data; visualization. After setting system up and testing it will provide a benefit. At first it gives 24/7 access to “clean”, checked “in-situ” data, ready for further research. Also presence of such system gives “converse effect” - it will become necessary to develop strict protocols for measurements carrying out and increase their quality. In future, “InSitu-Eye” can become a platform, connecting research teams for data keeping and exchange.

A conceptual model containing reflectance in three spectral bands in the red and near infrared ranges of the spectrum can be used to retrieve vegetation pigment concentration. Based on this model, the bio-optical properties of Chlorophyll-a (Chl-a), suspended solids, dissolved organic matter and water molecules were analyzed in this paper, by using in-situ spectra data, optical parameters and water quality parameters in Taihu Lake. Under the band range determined by spectral feature analysis, the optimal combination of bands (678 nm, 696 nm and 748 nm) was selected through the iterative method, to compose an optimized band combination in order to build the Chl-a semi-analytical model. Based on hyper-spectral imager (HSI) data carried on the Environmental 1 (HJ-1) satellite, this model was used successfully to retrieve the Chl-a concentration in Taihu Lake.

This study selects the typical middle and lower reaches of Han River as the study area and focuses on water quality evaluation methods and water quality evaluation of the surface water of the river basin. On the basis of the field survey, the author conducted a water quality sampling survey in the study area in spring and summer in 2012. The main excessive factors in the study area are determined as TN and TP. Using HJ1A/1B CCD multi-spectral data, the multiple linear regression inversion model and neural network inversion model are established for content of TN and TP. In accordance with these inversion results, the single factor water quality identification indexes in the study area are obtained. The results show that, BP neural network model boasts the highest inversion accuracy and that the single factor water quality identification indexes resulting from its inversion results are highly accurate, reliable and applicable, which can really reflect the changes in water quality and better realize the evaluation of water quality in the study area. Water quality evaluation results show that the water pollution in the study area is organic pollution; the water quality of Han River experiences large differences in different regions and seasons; downstream indexes are superior to upstream indexes, and the indexes in summer are superior to those in spring; the TN index seriously exceeds the standard in spring and the TP index seriously exceeds the standard in some regions.

To find a suitable water extraction method from Mapping satellite-1 imagery in urban areas, this paper compared the results of different water extraction methods and studied the effect of building shadow on water extraction. Taken parts of Xinjiang (Beitun of Irtysh river Basin) as the study area, Mapping satellite-1 imagery as data sources, single-band threshold method, Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and spectral relationship method based on spectral area were applied respectively to extract information of water body. The results of information extraction by use of the four methods were compared to determine the optimal method for water body extraction. The results showed that, for Mapping satellite-1 imagery, single-band threshold method and spectral relationship method which based on spectral area were effective to eliminate classification errors cased by shadows from buildings, fast, easy and accurate to extract information of water body from urban areas. In addition, the spectral relationship method based on spectral area also had the character of extracting small tributary rivers.

The objective of this paper was to evaluate the prediction ability of visible and near-infrared reflectance spectroscopy(VNIRP) to estimate soil total nitrogen concentration (TN) in the Sanjiang Yuan regions, Qinghai province, China by using partial least squares regression (PLSR) method. A total of 149 soil samples (0-30cm) were collected respectively from Yushu county and Maduo county in August 2012. The soil total nitrogen concentrations were measured using Vario EL Ⅲ elemental analyzer (Germany Elementar Inc), and the soil spectral reflectance of with spectral range 350-2500nm was measured using ASD FieldSpec 4 in the laboratory. PLSR model was used to relate the reflectance spectra and its five different pre-processing transformations including the first order derivative reflectance (FDR), the second order derivative reflectance(SDR), the logarithm of the inverse of the reflectance (Log (1/R)), the depth of the band (Band depth) and the first order derivative of the logarithm of reflectance (( Log R)′) to the soil total nitrogen contents measured. The results showed that the combination of visible-near infrared reflectance spectroscopy and PLSR to estimate TN produced a good prediction ability with R² < 0.76 and RPD < 2.01 in the Sanjiang Yuan regions. The best prediction ability was obtained by (Log R) ' with R²_CV = 0.87, R²_V =0.88 and RPD <2.67, followed by Log (1/R), Band Depth, SDR, FDR and Reflectance. Visible and near infrared spectroscopy provided a rapid and effective method for estimating soil TN from five different soil types in the Sanjiang Yuan Regions, Qinghai province.

With the development of Hyperspectra and the method of rock-mineral information extraction, several cores were analyzed based on analytical spectral devices (ASD) and rock-mineral information extraction in Wushan-cooper deposit area. Aiming at the low accuracy of mineral identification with hyperspectral data, the present study established regional spectra library on the basis of the study area geological background, section noise filtering and fast Fourier transform processing methods. Using the rapid quantificational identification model, the rock-mineral alternation information was extracted to build core profile and 3D model to discuss the deep mineralization evaluation. Combing with the regional metallogenic background, the alteration information indicated that the ore mineral was related with multiple alteration assemblages and there may be rock mass in deep space. The Cu element contents and ore mineral were closely related with the skarnization, silicification and chloritization. It also suggested that the deposit was skarn type in less than 1000 m depth, which was affected by the sandstone. Meanwhile, in more than 1000 m depth, the deposit was controlled by composite minerallzation types, which was associated with the previous geology and mineral deposits studies. In summary，this study supported a two stage mineralization model for the Wushan-copper deposit area，namely，the first stage of synsedimentary hydrothermal exhalative stage and the second stage of magmatichydrothermal ore-forming stage.

The mobile spectral line camera imaging currently develops quickly as a kind of new terrestrial hyper-spectral remote sensing technique with high potential of acquiring spatial information. However, vehicle moving with fluctuation tends to cause the recorded line images with spatial inconsistency. This study is to find a method to rectify the fluctuation effect common in mobile spectral line camera imaging. The used methods are as follows. First, road spectral characteristics were analyzed and the spectral features were extracted. Second, the morphological features of roads were derived, and the line images were rectified. The third step was to assess the result by comparing the rectified image to the reference acquired by artificial interpretation. The result proved to be basically positive. The rectified image can be applied for registration and fusion with point cloud of laser radar and CCD image for further application. The fusion data is of great significance for the extraction of road environment information and environment monitoring.

In order to take full advantage of the multi-source remote sensing data, the concept of image fusion was proposed and nowadays, many fusion algorithms have been produced, so the adaptability of the algorithm has become a hot topic. This article aiming at the panchromatic and multispectral images of Worldview-2, uses the different fusion methods, including PCA, HPF, Gram-Schmidt and wavelet transform, to fuse the image, and evaluates the results from the aspects of the statistical information, spectral information and spatial characteristics. Then it takes the extracting vegetation as an example, analyzes the applicability of different fusion methods. The research shows that Gram-Schmidt and wavelet transform have better fusion quality and Gram-Schmidt is the most suitable method to extract vegetation for Worldview-2.

The rapid increase in quantity of available remote sensing data brought an urgent need for intelligent retrieval techniques for remote sensing images. As one of the basic visual characteristics and important information sources of remote sensing images, texture is widely used in the scheme of remote sensing image retrieval. Since many images or regions with identical texture features usually show the diversity of direction, the consideration of rotation-invariance in the description of texture features is of significance both theoretically and practically. To address these issues, we develop a rotation-invariant image retrieval method based on the texture features of remote sensing images. We use the steerable pyramid transform to get the multi-scale and multi-orientation representation of texture images. Then we employ the hidden Markov tree (HMT) model, which provides a good tool to describe texture feature, to capture the dependencies across scales and orientations, by which the statistical properties of the transform domain coefficients can be obtained. Utilizing the inherent tree structure of the HMT and its fast training and likelihood computation algorithms, we can extract the rotation-invariant features of texture images. Similarity between the query image and each candidate image in the database can be measured by computing the Kullback-Leibler distance between the corresponding models. We evaluate the retrieval effectiveness of the algorithm with Brodatz texture database and remote sensing images. The experimental results show that this method has satisfactory performance in image retrieval and less sensitivity to texture rotation.

A great challenge in hyperspectral image analysis is decomposing a mixed pixel into a collection of endmembers and their corresponding abundance fractions. This paper presents an improved implementation of Barycentric Coordinate approach to unmix hyperspectral images, integrating with the Most-Negative Remove Projection method to meet the abundance sum-to-one constraint (ASC) and abundance non-negativity constraint (ANC). The original barycentric coordinate approach interprets the endmember unmixing problem as a simplex volume ratio problem, which is solved by calculate the determinants of two augmented matrix. One consists of all the members and the other consist of the to-be-unmixed pixel and all the endmembers except for the one corresponding to the specific abundance that is to be estimated. In this paper, we first modified the algorithm of Barycentric Coordinate approach by bringing in the Matrix Determinant Lemma to simplify the unmixing process, which makes the calculation only contains linear matrix and vector operations. So, the matrix determinant calculation of every pixel, as the original algorithm did, is avoided. By the end of this step, the estimated abundance meet the ASC constraint. Then, the Most-Negative Remove Projection method is used to make the abundance fractions meet the full constraints. This algorithm is demonstrated both on synthetic and real images. The resulting algorithm yields the abundance maps that are similar to those obtained by FCLS, while the runtime is outperformed as its computational simplicity.

Most of the pixels in thermal infrared remote sensing images are three-dimensional non-isothermal pixel, especially for the pixels with the size of meters, tens of meters or hundreds of meters which have received widespread attention in geoscience and remote sensing. Even though the sizes of some pixels reach centimeters, the three-dimensional non-isothermal phenomenon may still arise. So, it is very important to accurately determine the component temperatures in one pixel for the related researches in geoscience. The remote sensing data used to carry out the related inversion experiments in this paper was the airborne remote sensing data obtained by WSIS (Wide Spectrum Imaging Spectrometer) the imaging wave bands of which include VNIR (visible light and near infrared), SWIR (short wave infrared) and TIR (thermal infrared). Firstly, the components of all the pixels in the image were determined through the VNIR images using linear mixing spectral model. Secondly, the emissivity of each component in every pixel in the image was determined according to a prior knowledge base of emissivity of many surface features. Thirdly, the so called average temperature of every pixel was retrieved using the TES (temperature and emissivity separation) algorithm. The retrieved temperature was regarded as initial value. The multi-band equations were established after the linearization of Planck function, and the component temperatures of every pixel in the image were inversed. The results show that the accuracy of the component temperatures inversion in one pixel can be improved obviously, with the combination of the VNIR, SWIR and TIR images.

Image classification has evolved from per-pixel to sub-pixel and from sub-pixel to super resolution mapping approaches. Super-resolution mapping (SRM) is a technique which allows mapping at the sub-pixel scale. Super-resolution mapping proves to be the better approach for the accurate classification of coarse spatial resolution images and to resolve mixed pixels in the boundary of such images. The accuracy of the super-resolved output depends on the input derived from the soft classification technique. This paper aims to compare the potential of support vector machine (SVM), spectral angle mapper (SAM) and linear spectral unmixing (LSU) as inputs for super-resolution mapping. The fraction image, distance measure image and probability image obtained from linear spectral unmixing, spectral angle mapper and support vector machine respectively are used as an input for super resolution mapping designed on Hopfield Neural Network (HNN) for the Hyperion image of Peechi reservoir, south India. Effectiveness of the inputs is evaluated by estimating the water-spread area of the Peechi reservoir from each of the outputs. The results indicate that the accuracy of any super-resolution approach depends on the inputs from the soft classification approaches. The accuracy of the water spread area estimated from the classified outputs is 95.9%, 96.6% and 99.7% from LSU, SAM and SVM respectively as inputs for the SRM method. Thus, the HNN based SRM method proves to be better when the soft classification input is from SVM.

Each scene of image generated from earth observation satellites can only cover a certain area. When one scene cannot cover a user’s area of interest, two or more scenes are needed to be registered and combined into a single image, and this composition process is referred to as image mosaicking. The key issue in image composition is to decide where to place the seam line in overlapping region. The optimal seam line which joins several scenes of images covered the entire study area, is usually determined by texture and other characteristics of the overlap region for seamless quality. Recently, a morphological image compositing algorithm was proposed which is able to automatically delineate seam lines along salient image structures. And this algorithm uses the ideas of marker-controlled segmentation for image mosaicking and divides the overlap region into a determined number of areas. The resulting seam lines of the morphological image compositing algorithm cut along high gradient regions which are object edges in initial images. However, the morphological compositing method only applied to delineate the invisible seam line to the human eyes based on Landsat ETM+ data which is the representation of medium resolution data. In this paper, we test the validation of the morphological compositing method to generate visually pleasing seam line for image mosaic without changing the image radiometry and feasibility to handle two adjacent scenes simultaneously on high spatial resolution imagery by using ZY-3 multispectral image data. The focus of this paper is developing a quantitative evaluation measure which is usually formulated as the sum of morphological gradient of the image mosaic along the seam line divided by the length of the seam to quantitatively estimate the ‘quality’ of the automatically delineate seam line.

Multi-sensor image registration is an important part of the remote sensing image processing. The gray property of the same object would have large differences in infrared and visible imaging mode, so it could get less matching points by using traditional SIFT algorithm directly in registration. However, NSCT decomposition can represent the structural information of the image very well and extract more SIFT feature points in its high frequency decomposed image. In addition, traditional SIFT descriptors’ gradient is affected by gray contrast, which could get less feature matching points during the similarity search in the matching procedure. Gradient mirroring (GM) is a method that can modify the direction of the feature points, which can reduce the contrast impact on the similarity matching. Therefore, a novel method combining NSCT and GM is proposed in this article. The experiments prove that, comparing with the traditional SIFT algorithm, the new method can get more matching points, better distributing and higher matching rate in infrared and visible image registration.

This paper presents an efficient visibility method based on OpenGL in large-scale urban environment. Due to the full 3- D environment of macro cell, visibility judge will contain huge amount of buildings and environment information, which means extremely high computation time for traditional method. The model established by accurate digital map, depth test and color buffer is used here to assist in visibility judge, which the location and the height of antenna are of great important. This method is suitable for three-dimensional simulations.

A three-dimensional (3D) ray-tracing model with the use of the uniform theory of diffraction and geometrical optics in which multiple reflections and diffractions are considered is presented in this paper to predict the channel coverage prediction of indoor environments, such as the received power and the RMS delay spread. The prediction model requires databases for the buildings and the objects layout in the indoor environment, in which the electrical characteristics (permittivity, conductivity, etc.) must be included. Electrical parameters directly influence the calculation of the reflection and diffraction phenomena. However, one cannot easily derive the electrical parameters due to the complexity of real building walls and the indoor objects. As a result, it is of great interest to quantify the prediction errors as functions of the electrical parameters inaccuracies. The Fresnel reflection coefficient and the diffraction coefficient are considered assuming several values in two different methods. The first method is to change the permittivity or the conductivity of the internal wall, the ceiling and the floor at the same time. The other method is to change the permittivity or the conductivity of the indoor objects while keep the electrical parameters of the internal wall, the ceiling and the floor unchanged. The mean error and standard deviation between predictions results of the original database and the error database are presented and analyzed. It is found that the accuracy of the model is sensitive to the relative permittivity and conductivity, and the sensitivity study also leads to the improvement of the accuracy of 3D ray-tracing model in indoor environments.

Spectrum-controllable light sources are able to generate desired spectrums as specific requirements, which have attracted huge interesting in many fields, e.g. life science, machine vision, defense-related science and technology. A controllable light source in the range from 0.38 to 0.78 μm, which is based on a Digital Micromirror Device (DMD) and an Offner dispersion configuration with a convex grating, is proposed in this paper. Due to its fast speed, high fidelity and simplicity with all spherical elements, the Offner relay is chosen as the dispersion structure compared with other existing spectroscopic configurations. The DMD located on the spectral image plane of the dispersion configuration is used to choose spectral channels according to the desired spectral radiance distribution through modulating micromirrors. The optical system of the proposed digitally spectrum-controllable light source is designed and further simulated by Zemax.

The radiometric calibration of LWIR Hyperspectral imager Spectrometer is presented. The lab has been developed to LWIR Interferometric Hyperspectral imager Spectrometer Prototype(CHIPED-Ⅰ) to study Lab Radiation Calibration, Two-point linear calibration is carried out for the spectrometer by using blackbody respectively. Firstly, calibration measured relative intensity is converted to the absolute radiation lightness of the object. Then, radiation lightness of the object is is converted the brightness temperature spectrum by the method of brightness temperature. The result indicated †that this method of Radiation Calibration calibration was very good.

The application of high solution thermal infrared imaging system highly relies on the signal to noise ratio (SNR) of instruments to acquire images. To enhance the imaging quality of the thermal infrared imaging system, the noise model for images of the thermal infrared imaging system has been analyzed. Spatial noise elimination method is proposed, including the results of the comparison between different methods. With combination of the testing data of the thermal infrared imaging system, it is showed that the method mentioned in this paper can really enhance the spatial-domain NETD and spatial resolution of images effectively.

Hyperspectral Imaging Systems (HIS) are widely applied in many fields. However, in the traditional design of HIS, it is much time-consuming to acquire an integrated hyperspectral image. Compressive sensing is an efficient method to process sparse data, and a single-pixel camera which used the digital micromirror device (DMD) for accomplishing the CS algorithms had been developed. Nowadays, DMD achieved great development. The size of mirror array is increasing while switch speed of a single mirror becomes very fast. Consequently, researchers make efforts to design a HIS using CS method. CS method is a method to scale down the spatial information but the hyperspectral datacubes are still huge because of the thousands of bands. In this paper, we design a DMD-based spectrometer architecture using the method of compressed sensing principle, combined with DMD's spectral filter characteristics. In the new architecture, there are two DMDs. One is used for implementing the CS pattern, the other for filtering the bands. It has spectral simply adjustable advantages. With this new technology, we can reduce the amount of information which needs to be transmitted and processed in both spatial and spectral domain. We also present some simulation results of implementation procedures.

In this study, we introduce a new spectroscopy analysis instrument, along with applied research based on the near-infrared spectroscopy (NIRS) of the major components of milk. Firstly, we analyzed and compared the characteristics of existing near-infrared spectrometers. Then, according to the major component spectra of milk, the spectral range, spectral resolution, and other parameters of the analysis instrument were determined, followed by the construction of a spectroscopy-analysis instrument based on acousto-optic tunable filters (AOTFs). Secondly, on the basis of application requirements, we obtained spectral information from a variety of test samples. Finally, qualitative and quantitative testing of the major components of the milk samples was carried out via typical analysis methods and a mathematical model of NIRS. Thus, this study provides a technical reference for the development of spectroscopy instruments and their applied research.

This paper describes a single-pixel-detection computational imaging approach using outdoor natural light. We produced an imaging prototype based on digital micro mirror (DMD).The prototype is equivalent to an optical computing system. Measurement of the input scene through band pass filter is equivalent to projective measurement in the transform domain, and hence can be treated with the Hadamard Transform or the Compressive Sensing frameworks recently developed by a number of groups. Computational imaging system offers a versatile approach to detect images' signal which can improve size, weight and easily controllable compared with traditional imaging approaches. Finally, we conducted some indoor and outdoor experiments for our imaging prototype, and also analyzed the differences between Hadamard Transform imaging and compressive sensing imaging.

Remote sensing technology takes the important role in the geological features identification of structure, strata, alterations and plutons. This paper uses satellite high spatial resolution remote sensing image and information identification technology to analyze tectonic geological features in Yinchuan basin, and better results are achieved. Based on image enhancement and geological interpretation, the structural characteristics including Helan mount large fold belt, Weining-beishan sawtooth structure and en echelon faults of Yinchuan area are analyzed and combined with seismic information, the causes of Yinchuan graben and its surrounding structures are discussed. Results shows all those structural characteristics and phenomena are caused by Yanshan movement and Himalayan movement, and Yinchuan graben is formed in the pulling tension environment caused by Himalayan movement on the basis of Helan-Hengshanbu fault belt which created by Yanshan movement. The tectonic evolution period times are determinated by this results and it is important for oil and gas exploration. Remote sensing information identification technology provides the new perspective and approach for geological research.

Graphite is one of the important industrial mineral raw materials, but the high content of heavy metals in tailings may cause soil pollution and other regional ecological environmental problems. Luobei has already become the largest production base of graphite. To find out the ecological situation in the region, further ecological risk analysis has been carried out. Luobei graphite mine which is located in Yabdanhe basin has been selected as the study area, SVM classifiers method with the support of GF-1 Satellite remote sensing data has been used, which is the first high-resolution earth observation satellite in China. The surrounding ecological environment was monitored and its potential impact on the ecological environment was analyzed by GIS platform. The results showed that the Luobei graphite mine located Yadanhe basin covers an area of 499.65 km², the main types of forest ecosystems ( 44.05% of the total basin area ), followed by agricultural area( 35.14% ), grass area( 15.52% ), residential area ( 4.34% ), mining area ( 0.64% ) and water area( 0.30% ). By confirming the classification results, the total accuracy is 91.61%, the Kappa coefficient is 0.8991. Overall, GF-1 Satellite data can obtain regional ecosystems quickly, and provide a better data support for regional ecological resource protection zone. For Luobei graphite mines area, farmland and residential areas within its watershed are most vulnerable to mining, the higher proportion of farmland in duck river basin. The regulatory tailings need to be strengthened in the process of graphite mining processing.

This paper presents a novel method for hyperspectral image classification based on the minimum noise fraction (MNF) and an approach combining support vector machine (SVM) and linear discriminant analysis (LDA). A new SVM/LDA algorithm is used for the classification. First, we use MNF method to reduce the dimension and extract features of the image, and then use the SVM/LDA algorithm to transform the extracted features. Next, we train the result of transformation, optimize the parameters through cross-validation and grid search method, then get a optimal hyperspectral image classifier. Finally, we use this classifier to complete classification. In order to verify the proposed method, the AVIRIS Indian Pines image was used. The experimental results show that the proposed method can solve the contradiction between the small amount of samples and high dimension, improve classification accuracy compared to the classical SVM method.

In the field of lunar surface content analysis, spectral characteristics of the 2.2~3.2 μm waveband can provide a strong basis for analyzing and identifying specific compositions. For lunar soil, an object’s spectrum in this band includes not only reflective solar radiance, but also emissive radiance from the temperature characteristics of the object itself. These form a mixed spectrum, which complicates the spectral analysis for this band. Based on a mixed spectral model, considering both reflective and emissive radiance on the lunar surface, spectral characteristics for a 2.2~3.2 μm waveband are simulated and relationships are analyzed between reflective/emissive spectra and factors such as temperature, light, an object’s physical composition, etc. Moreover, a simulative platform (incorporating a spectrometer, light source, temperature controller, and simulative object) is developed according to lunar surface conditions, so as to further validate the mixed spectral model, which may provide a reference for lunar surface spectral analysis and the development of spectrometers.

Feasibility of using structured light for remote sensing and 3D damage assessments have been explored in this study. Although near real-time reconstruction of 3D objects in color is an active research area which possesses a wide variety of application including disaster assessments, its application to remote sensing is relatively new. Structured light can be applied to aircraft, UAV platforms, as well as provide in situ accurate disaster assessments. It also provides ground truth for validating satellite observations. The feasibility for this type of applications is investigated with model building and automobile experiments and has produced promising preliminary results.

Sea ice density is one of the significant physical properties of sea ice and the input parameters in the estimation of the engineering mechanical strength and aerodynamic drag coefficients; also it is an important indicator of the ice age. The sea ice in the Bohai Sea is a solid, liquid and gas-phase mixture composed of pure ice, brine pockets and bubbles, the density of which is mainly affected by the amount of brine pockets and bubbles. The more the contained brine pockets, the greater the sea ice density; the more the contained bubbles, the smaller the sea ice density. The reflectance spectrum in 350~2500 nm and density of sea ice of different thickness and ages were measured in the Liaodong Bay of the Bohai Sea during the glacial maximum in the winter of 2012-2013. According to the measured sea ice density and reflectance spectrum, the characteristic bands that can reflect the sea ice density variation were found, and the sea ice density spectrum index (SIDSI) of the sea ice in the Bohai Sea was constructed. The inversion model of sea ice density in the Bohai Sea which refers to the layer from surface to the depth of penetration by the light was proposed at last. The sea ice density in the Bohai Sea was estimated using the proposed model from Hyperion image which is a hyperspectral image. The results show that the error of the sea ice density inversion model is about 0.0004 g•cm^-3. The sea ice density can be estimated through hyperspectral remote sensing images, which provide the data support to the related marine science research and application.

Taking debris flow area as an example, this paper studied retrieval of soil Sodium content and pH by hyperspectral remote sensing, which provided a new method for estimating soil dispersion. Based on preprocessing, the authors extracted four spectral indices, including reflectance(R), inverse reflectance(1/R), inverse-log reflectance(log(1/R)) and band depth(BD), to establish the prediction model for Sodium content and pH using stepwise multiple regression method. Results indicated that reflectance spectra and inverse-log reflectance were the optimum parameters for inverting soil sodium ions content and pH, respectively. Determination coefficients R² of prediction samples were 0.690 and 0.641 respectively, and R² of test samples were 0.523 and 0.438, which showed that soil spectra with high spectral resolution had the potential for the rapid prediction of Sodium content and pH, thus, providing reliable detection method for soil dispersion using hyper-spectral technology.

Vehicle detection is a very important task for intelligent transportation system. In this paper, a method with mathematical morphology and template matching is presented to detect the crowded vehicles of parking lot with high resolution aerial image. Our experimental results with high resolution aerial image showed that the graded image, with the spatial resolution of 1×1ft, could greatly reduce the calculation time, but with the same accuracy as the original image with the spatial resolution of 0.5×0.5ft .

Digital elevation models (DEMs) of buildings in urban areas are becoming increasingly important for a large range of applications, whereas extracting building DEMs over a city is still a complicated and expensive task. In this paper, we present and evaluate two approaches for fast and handy extraction of building DEMs from ALOS PRISM stereo pairs. A Digital Surface Model (DSM) is firstly generated based on the stereo data. Approach 1 uses a digital terrain model (DTM) derived from topographic maps, and Approach 2 creates another DTM by removing buildings on the DSM through a filtering approach. Building DEMs are then acquired by subtracting the two sets of DTMs from the DSM, respectively. The results received with the two approaches are promising. A mean absolute error of 7.07 m and 7.37 m respectively is achieved. Approach 1 has better accuracy in areas with high-density buildings, while Approach 2 has better one in areas with low-density buildings. Combination of the two approaches would be a useful tool for extraction of building DEMs over a city.

Some results are given of the airborne applications to recognize forest classes of different species and ages for a test area based on the imaging spectrometer produced in Russia. Optimization techniques are outlined to select the most informative spectral bands for the particular subject area of the forest applications using the improved Bayesian classifier in the pattern recognition supervising procedures. A successive addition method is used in this optimization with the calculation of the probability error of the statistical pattern recognition while collecting the spectral ensembles for the known classes of forest vegetation for different species and ages. The subsequent step up method consists in fixing the level of the probability error that is not improved by adding the channels in the related computational procedures. The best distinguishable classes are recognized at the first stage of these procedures. The analytical technique called “cross-validation” is used for this purpose. The second stage is realized as a stable feature selection method based on the standard stepwise optimization approach, holdout cross-validation and resampling.

A hyperspectral full waveform LiDAR instrument prototype with 32 wavelengths and a supercontinuum laser as a light source were designed for monitoring the fine structure and the biochemical parameters of vegetation. The optical design and instrumentation are described in this paper. Components of the instrument included an X/Y scanning platform, a supercontinuum laser source, a receiving optical system, and a 32-channel full waveform measurement module. The pulsed LiDAR instrument is able to measure the 32-channel returned full waveform laser signal. The distance of the interacted target can be obtained from position information in the recorded waveform. And the spectral reflectance can also be obtained from the intensity information in the waveform. The performance of the measuring distance and spectrum was evaluated. The initial performance trials indicated that the instrument is capable of high measurement accuracy and has the ability to detect the biochemical characteristics of vegetation. The experiment also indicated that the instrument has the potential to generate a 3D point cloud with spectral information. Therefore, the instrument can play a significant role in detecting the vertical distribution of biophysical and biochemical characteristics of vegetation.

Hyperspectral remote sensing has one of the technical advantages atlas. The known deposits of Gansu Beishan South Beach deposits as the study area, based on the theory of wall rock alteration, using airborne hyperspectral remote sensing data (CASI/SASI), extracted mineralization alteration information and analysis. Based on airborne hyperspectral remote sensing mineral mapping results in the study area, Combining analysising of possible mineral formation fluid properties, spatial distribution characteristics and time evolution with analysising of mineral formation environment (lithology and tectonic environment), construction of the South Beach gold deposit location model, the deposit location model as a guide, comprehensive analysis of mineralization geological background and surface geochemical data, delineated mineralization favorable areas. The field investigation showed that signs of altered development of strong in the delineation of the mineralization favorable areas and metallogenic potential of better, is worth paying attention to the prospecting target area. Further explanation that the hyperspectral remote sensing can provide accurate and reliable information for the prospecting, and is worthy of further mining the ore prospecting potential.