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This PDF file contains the front matter associated with SPIE
Proceedings Volume 6787, including the Title Page, Copyright
information, Table of Contents, Introduction, and the
Conference Committee listing.
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In this paper, a self-adaptive evolutionary clustering algorithm is presented. This algorithm uses the evolutionary programming (EP) to search the optimal clustering and bases on the principles of the
K-means algorithm. The proposed self-adaptive evolutionary (SAEP) clustering algorithm self-adapts the vector of the step size appropriate for each parent. This is different from other
genetic-based algorithms. The algorithm can minimize the degeneracy in the evolutionary process. The experimental results show that the KSAE clustering algorithm is efficient in the unsupervised classification of the multispectral remote sensing image.
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In this paper, the problem of the detection of road networks in Synthetic Aperture Radar (SAR) images is addressed. Most of the previous methods extract the road by detecting lines and network reconstruction. Traditional algorithms such as MRFs, GA, Level Set, used in the progress of reconstruction are iterative. The tensor voting methodology we proposed is non-iterative, and non-sensitive to initialization. Furthermore, the only free parameter is the size of the neighborhood, related to the scale. The algorithm we present is verified to be effective when it's applied to the road extraction using the real Radarsat Image.
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Through analyzing on data acquisition mechanism of LiDAR's multi-return signal, we deduce the relational model of microtopography and corresponding return signal, further research the rapid extract algorithms about terrain slopes, roughness and others terrain parameters. And then validate them by consecutive return signal data of GLAS, and field experiment.
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Ideally, one objective of image fusion in remote sensing is to obtain high-resolution multispectral images with simultaneously the spectral characteristic of multispectral images and an enhanced spatial resolution. To date, numerous image fusion techniques have been developed. However, many methods may introduce spectral distortion, appearing as a change in colors between compositions of resampled and fused multispectral bands. To tackle this problem, some methods have taken the radiometric characteristics of sensors into account. This paper is an attempt to fuse high-resolution panchromatic and low-resolution mutlitspectral bands of the EO-1 ALI sensor. Starting from the analysis of spectral difference between ALI and other sensors, the authors present two methods which take into account the physical spectrum response of sensors during the fusion process: one is an improved fast intensity-hue-saturation (IHS) method with spectral adjustment according to sensor spectral response, and the other directly introduces sensor spectral response into the general component substitution image fusion method. An experiment based on ALI images has been carried out to demonstrate the effectiveness of the proposed approach. The fused images processed through the proposed methods have almost the same spatial resolution as panchromatic images and keep good spectral characteristics.
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Atmospheric correction, which can retrieve water-leaving radiance, is an important preprocess in monitoring water quality from remote sensing data. The atmospheric correction algorithms developed by Gordon (1993, 1994) assume that water-leaving radiance of ocean waters in near infrared is zero. However, such an assumption is not applicable to inland waters, and usually leads to failure in atmospheric correction of remote sensing data of inland waters. Some scientists, based on some other assumptions, have developed some improved atmospheric correction algorithms which can be applied to coastal and inland waters. However, these algorithms can only get good results in specific areas. In order to get good results of atmospheric correction of remote sensing data of inland waters in China, an improved atmospheric correction algorithm is developed in this paper. This improved atmospheric correction algorithm assumes that water-leaving radiance in short-wave infrared is zero, which is based on the analysis of absorption and scattering characteristics of inland waters. This atmospheric correction algorithm is validated to have high applicable potentials by applied to concurrent MODIS data and in-situ measured reflectance spectra in Guanting Reservoir in North China.
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Most of the feature extraction methods is complex and is easily influenced by some uncertain factors. Based on the idea of multi-resolution decomposing theory, a new statistical feature extraction algorithm based on gray multi-channel decomposition is proposed in the paper. The image's gray is decomposed into some channels. The gray of an image is partitioned into some gray regions, which is also called gray channels. The gray features on each channel are analyzed and extracted. Then all the gray features form feature vector. The multi-channel feature extraction method based on multi-resolution decomposition using wavelet theory is introduced at first. Each channel's gray feature is extracted and formed the feature vector. Targets can be well distinguished. But the method not has invariant property when image rotates. Referenced the thought of multi-resolution decomposition, a new multi-channel feature extraction based on gray is proposed in the paper. The four features, which is average of gray, variance, number of pixel and peakedness, are extracted and form the feature vector. The vector is used to identify targets. With the new method, the shortage of the former method is overcome. And the method not only has invariant property of rotation, but also has invariant properties of proportion and translation. The robustness is perfect. And the calculation is simple. The features of some infrared images of tank and helicopter are extracted with the new method. The results show its effectivity. It's helpful for image feature extraction and target recognition.
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This paper presents a hardware-based implementation, which is specially designed for fast processing data cube of the novel imaging spectrometer, the Computed-Tomography Imaging Interferometer (CTII). CTII is the combination of the conventional Fourier Transformation Imaging Spectrometer (FTIS) and the Computed-Tomography Imaging Spectrometer (CTIS). CTII retains the advantages of both FTIS and CTIS such as high etendue, high spectral resolution. The price paid for above advantages is much enormous amounts of calculation compared with FTIS and CTIS. The CTII algorithm mainly includes two processing parts. One is Fast Fourier Transformation (FFT) Algorithm, the other is the Convolution Back-Projection (CBP) Algorithm. Since these two parts can be dealt separately, we introduced the pipeline processing technology. In this paper, we develop a hardware-based version of the CTII algorithm to improve its computational performance by resorting to Field Programmable Gate Arrays (FPGA) reconfigurable boards. The proposed FPGA design represents our first step toward the development of a system for onboard analysis of CTII hyperspectral imagery. The results we obtained demonstrate that the concept of the Computed-Tomography Imaging Interferometer (CTII) is correct, and the hardware-based version of the CTII algorithm is high efficiency in processing CTII data cube.
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In this paper the simulated space-based high spectral resolution atmospheric infrared sounder (AIRS) infrared radiances with different cloud top heights and effective cloud fractions are used to demonstrate the measurement sensitivity and atmospheric profile retrieval performance. The simulated cloudy retrieval of atmospheric temperature and moisture derived from the statistical eigenvector regression algorithm are analyzed with different effective cloud fractions and different cloud height. The temperature and humidity root-mean-square error with cloud fraction ranging from 0.1 to 1.0 (with interval of 0.1) for cloud height (200, 300, 500, 700 and 850 hPa) known perfectly and cloud height error of 50 hPa are computed. Results show that the root-mean-square error of retrieved temperature and the mixed ratio of water vapor below the cloud top increase with effective cloud fraction. The retrieval accuracy of the cloud height error of 50 hPa decrease comparing with the cloud height known perfectly, while the temperature retrieval is more sensitive to cloud height error than humidity retrieval.
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Aiming at the requirement of high frame frequency, real-time processing of detecting and tracking target in modern IR defense and detecting system, this article introduces the design and engineering implementation of a portable IR target automatic detecting and tracking system. Firstly analyzes the overall structure and introduce the composition and function in detail. Secondly puts forward the flow of low-altitude infrared small target detecting and tracking algorithm, then researches the background suppressing and detecting technology of far-distance weak small targets with low SCR. Finally aiming at the key technology of real-time processing with the limit of volume and power, a project of real-time signal processor that is reconfigurable based on DSP+FPGA is proposed and implemented, which sends the images and detecting results to host computer by USB2.0 interface. Besides, the hardware architecture, the target detecting flow in DSP, the driver and the firmware of USB2.0 are introduced too. The experimental result shows that this system can detect and track the target automatically and accurately, which satisfy the requirement of the real-time performance of the system.
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Phase unwrapping is the key step in deriving topographic information from Interferometric SAR. We propose a region-cutting InSAR phase unwrapping method based on Shortest Distance First Principle(SDFP), the interferogram is cut into the residue regions which contain all the residues and the non-residue regions which contain no residue. The pixels of the non-residue regions are unwrapped using modified Itoh's method firstly while the residue regions have been isolated, then region-growing strategy of region-growing phase unwrapping algorithm is applied to unwrap the pixels of residue regions from the unwrapped non-residue regions. The test performed on the real wrapped phase data obtained from the interferometry of a pair SAR images shows the proposed algorithm is fast and efficient even in
low-quality regions.
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Image fusion is an important tool in remote sensing, since many Earth observation satellites provide both high-resolution panchromatic (Pan) and low-resolution multispectral (MS) images. To date, many image fusion techniques have been developed. However, the available algorithms can hardly produce a satisfactory fusion result for IKONOS and QuickBird images. Among the existing fusion algorithms, the IHS technique is the most widely used one, and the wavelet fusion is the most frequently discussed one in recent publications because of its advantages over other fusion techniques. But the color distortion of these two techniques is often obvious. The support value fusion technique demonstrates some advantages over the conventional methods. This study presents a new fusion approach that integrates the advantages of both the IHS and the support value techniques to reduce the color distortion of QuickBird fusion results. Different QuickBird images have been fused with this new approach. Visual and statistical analyses prove that the concept of the proposed extended fast IHS (eFIHS) and support value integration is promising, and it does significantly improve the fusion quality compared to conventional IHS (eFIHS) and wavelet fusion techniques.
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Proposed is a novel approach based on independent component analysis (ICA) for speckle reduction and target extraction of SAR (synthetic aperture radar) images using adaptive space separation with weighted information entropy incorporated. First the basis and the independent components are respectively obtained by ICA technique, and Weighted Information Entropy of the image is computed, then based on the threshold computed from function T-WIE (Threshold VS.
Weighted-Information-Entropy), independent components are adaptively separated and the basis are classified accordingly. Thus, the image space is separated into two subspaces -'clean' and 'noise'. Then, a proposed nonlinear operator 'ABO' is applied on each component of the 'clean' subspace for further optimization. Finally, recovery image is obtained reconstructing this subspace and target is easily extracted with binarisation. Note that here T-WIE is an interpolated function based on several representative target SAR images using proposed space separation algorithm.
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Since 1986 Bayesian Network has been a hot study topic in artificial intelligence field. We have researched Bayesian Network and related algorithms in remote sensing data processing for five years. Recently we finished BayesNetEX which is an extensive edition software of Bayesian network algorithms for remote sensing image processing and knowledge inference. The copyright is from Copyright Protection Center of China. This paper briefly introduces the main modules of BayesNetEX and demonstrates its classification application with an ETM+ image, which also shows some potential applications in remote sensing image processing.
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This paper studies the algorithm fusing IR/Visible images used in imaging guidance field. Firstly, the differences between IR image and Visible one are introduced curtly. Then we introduce the essential multiwavelet theory and explain the reason why we choose multiwavelet analysis as a tool fusing images. We give the framework of image fusion based on discrete multiple wavelet transform (DMWT) which core principle is still based on multiresolution analysis (MRA) and Mallat algorithm. A novel image fusion method based on multiwavelet transform and directional contrasts information is presented.
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Pattern Recognition has been successfully applied to target detection. The characteristic of target pattern determines the detection ability in pattern recognition. The pattern of spectral signature at hyperspectral resolution provides more distinguished spectral feature for target detection and then improves the detection ability. However, hyperspectral image has limitation on low resolution in spatial. Therefore, this article focus on analyze on the target detection ability at the sub-pixel scale in different spatial resolution, considering two critical factors, i.e. spatial response in sensor and background interferer. Experiment data is simulated by inducing these two factors. Target-to-Clutter-Ratio (TCR2) curve and Receiver Operating Characteristic (ROC) curve with Uniform Target Detector (UTD) analyze on the simulated data. We conclude that spatial response of the sensor and the background interferer induce uncertainty into target detection ability and usually weakens it. It gives rise to a new requirement for hyperspectral target detection that should be considerate for the effect caused by spatial resolution.
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In this paper, an algorithm for hyperspectral image compression is presented. It carries DCT (Discrete Cosine Transform) on spectral bands to exploit the spectral correlation and then DWT (Discrete Wavelet Transform) on every eigen image to exploit the spatial correlation. After that, 3D-SPIHT (three-dimensional Set Partitioning in Hierarchical Trees) is performed for encoding. Experiments were done on the OMIS-I (Operational Modular Imaging Spectrometer) image and the performance of this algorithm was compared with that of
2D-SPIHT. The results show that the performance of 3D-SPIHT based on DCT and DWT is much better than that of 2D-SPIHT and the quality of the reconstructed images is satisfying.
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The at-surface radiances of a thermal infrared channel relates to the land materials characters, which is composed of the thermal radiance of the land itself and the atmospheric downwelling radiance reflected by the land surface. This parameter is very important for thermal infrared remote sensing, but the researches about its retrieving from the remote sensors with a wider spectral range are less. This paper discussed a method to retrieve the parameter by using the NOAA-AVHRR image itself. The method was firstly theoretically deduced based on the thermal radiance transmission theory. According to the deduction, the atmospheric transmittance and the upwelling radiance of the imaging time can be expressed by the relation of two channels
at-sensor radiances, which are used to calculate the at-surface radiances. Then the simulated data were used to determine the model's coefficients and prove the feasibility of the theoretical retrieval method. The relative root mean square error for the research method was about 5.0 percent by comparing with the validation data, which showed that the method could simply and effectively compute the at-surface radiance of the two thermal infrared channels by the image itself for the AVHRR sensors. Even if lacking of the synchronous atmospheric parameters, a higher retrieval precision also exist.
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Airborne laser scanning data has become an accepted data source for highly automated acquisition of digital surface models(DSM) as well as for the generation of digital terrain models(DTM). To generate a high quality DTM using LIDAR data, 3D off-terrain points have to be separated from terrain points. Even though most LIDAR system can measure "last-return" data points, these "last-return" point often measure ground clutter like shrubbery, cars, buildings, and the canopy of dense foliage. Consequently, raw LIDAR points must be
post-processed to remove these undesirable returns. The degree to which this post processing is successful is critical in determining whether LIDAR is cost effective for large-scale mapping application. Various techniques have been proposed to extract the ground surface from airborne LIDAR data. The basic problem is the separation of terrain points from off-terrain points which are both recorded by the LIDAR sensor. In this paper a new method, combination of morphological filtering and TIN densification, is proposed to separate 3D off-terrain points.
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Super-resolution reconstruction has been widely used in infrared images. A lot of effective super-resolution methods have been presented in recent years. In this paper, a fast and robust super-resolution algorithm based on Maximum a Posteriori (MAP) estimation is proposed to obtain a high resolution image from a set of infrared images, which are obtained by an uncooled infrared detector. A comparison and an analysis are made of the super-resolution reconstruction results by this method, with the variance of regularizations and the number of low resolution infrared images, by direct observation and the value of Power Signal-to-Noise Ratio (PSNR). Simulation results with several real sets of infrared images show the effectiveness and superiority of this method for enhancing resolution of infrared images.
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In this paper, we present a new iterative blind multispectral image restoration algorithm based on double regularization (DR). The motivation for DR when applied to multispectral restoration lies in its effectiveness towards edge preservation in joint blur identification and image restoration. With consideration for both the intra- and inter-channel blurring function in the multiple-input multiple-output (MIMO) systems, an alternating minimization (AM) procedure with conjugate gradient optimization (CGO) scheme is formulated to implement restoration iteratively. The derivation of DR optimization shows that optimal restoration result can be achieved even when the MIMO systems suffer from inter-channel interference. Experimental results show that it is effective in performing blind mutichannel restoration when applied to color images.
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This paper developed a new model of region extraction based on salient region detection and scale-space primal sketch. In the proposed model, we extract the region of interest (ROI) in two steps. Firstly, we estimate the extent of object by means of region detection, which considers the feature that contributes most to the saliency map. Secondly, we use the scale-space primal sketch to acquire an explicit representation of the significant image structure which gives a qualitative description of the scales and regions of interest. Finally, we combine the results from the two steps. Applications to extract ROI showed that this new model could lead to better results which can be used for guiding later stage processing.
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In this paper, a new image fusion frame based on nonsubsampled contourlet transformation is provided. In this frame, a new method based on the local correlation coefficients is explored, the experiment results of this method can obtain relatively high spatial resolution and preserve high spectral resolution.
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Image fusion based on wavelet transform is the most commonly used image fusion method, which decomposes the source images, fuses their coefficients according to some fusion rules and then reconstructs the fused image. Its two main traditional rules are selecting maximum absolute value and the combination of selecting and weighted averaging. Both of the two rules did some artificial supposes to eliminate the uncertainty of the extent of each source image's contributions, so they both ignored some useful information and were sensitive to noise. Fuzzy reasoning is the best way to resolve uncertain problems. As a result, this paper proposed a new image fusion algorithm based on wavelet transform and fuzzy reasoning. It first decomposed source images through wavelet transform, computed the extent of each source image's contribution through fuzzy reasoning using the area feature of source images' wavelet coefficients, and then fused the coefficients through weighted averaging with the extents of each source images' contributions as the weight coefficients. Finally it did inverse wavelet transform to produce the fused image. Using the mutual information and PSNR as criterions, experiment results demonstrated that the new algorithm was more effective and robust than the traditional fusion algorithms based on wavelet transform.
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We present an effective image recovering method .Edge information are very important, we can detect the edge of
the image via wavelet transform before recovering the image. In this paper, we give a simple and effective method to
detect the edge information. The first, we can decompose the image using a wavelet transform, the high frequency
information is corresponding to the edge and noise, the edge and noise have different properties, as follows: the
orientation of edge is very strong, but the noise is non-orientation. On base of that, we can decompose the edge and noise
in horizontal orientation, vertical orientation, and diagonal orientation. Because the edge is directional, its values in each
orientation have much difference; in opposition, noise is non-orientation, its values are almost same. We can set the
threshold based on the variances of difference in each orientation. By this time, we gain the edge without noise. For the
rest, we can restore using regularization. Now the low frequency is corresponding to the flat. In this paper, we detect the
edge by the wavelet, so can choose a bigger parameter to recover. At last, we add the edge and the part of regularized
image restoration. This method has advantage in holding the edge and is simple to choose the parameter of regularization.
Experimental results show the good performance, this method can keep image's of edge from degradation and increase
PSNR up to 1~2dB.
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An improved local RX anomaly detection algorithm is proposed. It firstly projects the images onto the background orthogonal subspace to make local data closer to multivariate normal distribution. Then for every tested pixel in the center of the sliding local window, the bands used in RX detector are chosen adaptively. To avoid the influence of anomaly information on the background characteristic statistic, the anomalous pixels in the local background are removed and the covariance matrix is calculated using real background pixels. Finally the RX detector is used to calculate the anomalous degree of every tested pixel. Experimental results indicate it is robust and has good anomaly detection performances under complex unknown background.
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In this paper, we propose a new de-noising method by wavelet transform based on lifting scheme for the reducing of the multiplicative speckles in synthetic aperture radar (SAR) images. An ERS-2 SAR image of Hangzhou was used as a test image to compare the performance of the method with that of conventional methods. The results show that the proposed method has advantages in radiation characteristics and textual details of the image over the conventional methods.
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Color fusion algorithm for visible and infrared(IR) images based on color transfer in YUV color space under trees, lawn, or land background is presented. Considering the red color will alert observers to possible interested target or danger, this paper aims at working on an algorithm that emphasizes hot targets in IR image with intense red, and the background details in visible image present natural color similar to a color day-time image. V component of YUV space represents the difference between red and Y. Properly increasing the V value will obtain intense red color. Therefore, a nonlinear transfer method based on local mean value of the IR image is proposed. A window of size 5x5 is used to locate hot target in IR image. When the local gray mean value in this window is larger than the global mean value, we determine that this pixel is in a hot area. Then its V value is increased by the ratio of the local gray mean value to the global mean value. Tests show that this method pops out the hot targets with intense red color while the background rendered natural color appearance.
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Aiming at the problem of negative index in the spectral color space built by means of traditional principal component analysis (PCA), a method of color component prediction based on rotated principal component analysis (RPCA) is proposed, which performs the rotating transformation from initial eigenvectors to a set of all-positive vectors as the physical basis color components while retaining the cumulative ratio of the variance contributions of significant principal components to the original multispectral space to the maximum extent. The rotated column vectors should be also polarized between 0 and 1. The spectral database of Munsell Matte Collection I is used for experiment. The experimental results show that the novel method of prediction not only uncovers the real color components of the target image better but reconstructs the normalized spectra data set with a high colorimetric and spectral accuracy. Thereinto, the colorimetric errors of the four estimated components reconstruction for more than 96 percent of the samples in Munsell Matte Collection I are less than 3 units of color difference acceptable.
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Ocean features in synthetic aperture radar (SAR) images are usually complicated and make SAR images hard to understand. Because of lower signal-to-noise rate (SNR) in SAR imagery, it is much more difficult to separate different ocean features than to separate different land features. This paper presents a completely novel method to separate ocean features from multi-band polarimetric SAR imagery based on polarimetric signatures of ocean features. AIRSAR data from Jet Propulsion Laboratory (JPL), National Aeronautics and Space Administration (NASA) are used in the case studies and good results are achieved.
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In this paper, we propose a new method to generate complex IR scene image directly from the corresponding visual scene image based on material thermal database. For the input visual scene image, we realize an interactive tool based on the combined method of global magic wand and intelligent scissors to segment the object areas in the scene. And the thermal attributes are assigned to each object area from the thermal database of materials. By adopting the scene infrared signature model based on infrared Physics and Heat Transfer, the surface temperature distribution of the scene are calculated and the corresponding grayscale of each area in IR image is determined by our transformation rule. We also propose a pixel-based RGB spacial similarity model to determine the mixture grayscales of residual area in the scene image. To realistically simulate the IR scene, we develop an IR imager blur model considering the effect of different resolving power of visual and thermal imagers, IR atmospheric noise and the modulation transfer function of thermal imager. Finally, IR scene images at different intervals under different weather conditions are generated. Compared with real IR scene images, our simulated results are quite satisfactory and effective.
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Terrain aided navigation (TAN) is an efficient way to periodically correct the error accumulation of INS. The imaging laser radar is an ideal imaging sensor in TAN for the low-flying aircraft and unmanned air vehicles for the high precision multi-dimensional data acquisition capability and concealable attribute. In this paper, a new framework for applying the laser radar to terrain aided navigation is put forward. Then a new adaptive fused Kalman Filter is proposed to improve the accuracy and robustness. At last, the key factors affected the algorithm are analyzed and the comparative experimentations are presented. The simulating experiments show that the proposed algorithm improves the location accuracy, and has good initial error tolerance and fine robustness. It shows that this approach is a valid solution for the application.
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When designing the system of the infrared (IR) small target detection, both which detection algorithm to be chosen and how to set some parameters of the detection algorithm need to evaluate the complexity of the infrared backgrounds. In this paper, an effective method to evaluate the complexity of the sea-sky infrared backgrounds based on texture analysis is proposed. According to the characteristics of Infrared backgrounds, three statistical descriptors based on gray level co-occurrence matrix are calculated, and one statistical descriptors based on edge information is calculated. All statistical descriptors are fused to evaluate the complexity of IR backgrounds finally. Real and synthetic IR images under sea-sky background are applied to validate the proposed approach. Compared to existing methods, Experimental results demonstrate the robustness of the proposed method with high performance.
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With the extensive application of mobile robots in many different fields, map building in unknown environments has been one of the principal issues in the field of intelligent mobile robot. However, Information acquired in map building presents characteristics of uncertainty, imprecision and even high conflict, especially in the course of building grid map using sonar sensors. In this paper, we extended DSmT with Fuzzy theory by considering the different fuzzy
T-norm operators (such as Algebraic Product operator, Bounded Product operator, Einstein Product operator and Default minimum operator), in order to develop a more general and flexible combinational rule for more extensive application. At the same time, we apply fuzzy-extended DSmT to mobile robot map building with the help of new
self-localization method based on neighboring field appearance matching( -NFAM), to make the new tool more robust in very complex environment. An experiment is conducted to reconstruct the map with the new tool in indoor environment, in order to compare their performances in map building with four T-norm operators, when Pioneer II mobile robot runs along the same trace. Finally, a conclusion is reached that this study develops a new idea to extend DSmT, also provides a new approach for autonomous navigation of mobile robot, and provides a human-computer interactive interface to manage and manipulate the robot remotely.
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Contourlet Transform is efficient for image representation due to its capability of capturing smooth contours in natural images and hence its ability for natural images denoising is promising. Because the transform is not orthogonal, the shrinkage factors for images denoising with contourlet transform method is always estimated by Mento-Carlo method which is much time consuming and the recovered images are always somewhat blurred especially in high intensity noise conditions. To overcome the two deficiencies, we proposed a modified approach which we call subband effect factors method. Finding the factors is less time cost and it's much more efficient for images denoising than Mento-Carlo method by contourlet transform. Using the factors, we present a hard threshold denoising method by modifying the 3σ rule utilizing subband threshold effect factor of each subband in second version contourlet domain. The threshold effect factor of every subband is acquired according to the characteristics of every subband response driven by a normalized white Gaussian noise image. Experimental results show that, for natural and SAR images corrupted by Gaussian white noise, the denoising results including PSNR (peak signal-noise ratio) and visual quality are superior to those of Mento-Carlo method, especially when the noise is high intensity.
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In the past few years, wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the deficiency for taking account of intrascale correlations that exist among neighboring wavelet coefficients. In this paper, we propose to develop a joint hidden Markov model by fusing the wavelet Bayesian denoising technique with an image regularization procedure based on HMT and Markov random field (MRF). The Expectation Maximization algorithm is used to estimate hyperparameters and specify the mixture model. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. It is shown that the joint method outperforms lee filter and standard HMT techniques in terms of the integrative measure of the equivalent number of looks (ENL) and Pratt's figure of merit(FOM), especially when dealing with speckle noise in large variance.
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Synthetic aperture radar (SAR) images are inherently affected by a signal dependent noise known as speckle, which is due to the radar wave coherence. In this paper, we propose a novel adaptive despeckling filter and derive a maximum a posteriori (MAP) estimator for the radar cross section (RCS). We first employ a logarithmic transformation to change the multiplicative speckle noise into additive noise. We model the RCS using the recently introduced Generalized Gaussian density function[1], Which was proved to be the best described for the SAR Amplitude. We estimate model parameters from noisy observations by means of second-kind statistics theory, which relies on the Mellin transform. Finally, we compare the proposed algorithm with several classical speckle filters applied on actual SAR images. Experimental results show that the MAP filter based on the Generalized Gaussian prior for the RCS is among the best for speckle removal.
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Urban is a kind of very complex living space, and includes many and many different objects. For urban imagery, it is hardly to get a satisfied classification result utilizing any kind of single classification method. Aiming at this problem, this paper adopts a kind of classification method based on stepping masking principle using the parallel-pipeline classification and the improved FCM method. With this classification principle, it not only enhances the computation efficiency of classification, but also realizes the accurate and reasonable classification to the different kinds of urban objects. Finally this paper evaluates the precision of classification results using the confusion matrix and the Kappa coefficient separately. Analyzing from the classification effect and precision, this algorithm can satisfy the requirement of classification in different level or the thematic mapping.
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A new method for estimating noise in hyperspectral images is described in this paper. It is based on the strong between-band correlation of hyperspectral images and the concept of local standard deviations of small imaging blocks. The new method can be used to automatically estimate noise for both radiance and reflectance images. Unlike other methods discussed in this paper, the new method is more reliable for estimating noise in hyperspectral images with diverse land cover types. We successfully applied the new method in estimating noise for Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data.
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This paper describes an algorithm framework for registration of airborne based laser scanning data (LIDAR) and optical images by using multiple types of geometric features. The technique utilizes 2D/3D correspondences between points and lines, and it could easily be extended to general features. In generalized point photogrammetry, all lines and curves are consists of points, which could be describe in collinear equation, so it could represent all kinds of homogeneous features in an uniform framework. For many overlapping images in a block, the images are registered to the laser data by the hybrid block adjustment based on an integrated optimization procedure. In addition to the theoretical method , the paper presents a experimental analysis the sensitivity and robustness of this approach
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A novel 3D terrain matching algorithm is presented in this paper. A terrain feature vector map (FVM), composed of local mean and local gradient, is employed to represent the terrain elevation map (TEM). Compared with traditional matching algorithm using the magnitude of gradient to match, the new algorithm uses each component of the gradient vector to match individually, and it is able to generate two interim matching positions. Different from traditional matching algorithms which usually estimate an optimum matching position under some criterions at the end, the new algorithm fused the two interim matching positions to generate a final matching position or refuse to position in order to increase the matching confidence, which is very important because it is hardly acceptable to employ a mismatched position to correct the error of Inertial Navigation System (INS). Due to the stability of terrain and the high-precision of lidar ranging, the mean of a sensed terrain elevation map (STEM) sized terrain is quite stable. So it is bestowed to accelerate the matching process and to reduce mismatches at different terrain heights. Compared with other mismatch-eliminated methods based on neural network (NN) or support vector machine (SVM), the new method do not need training samples and is more stable and robust. Experimental results show that the proposed algorithm is effective and robust.
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In this paper, a nonlinear model of nonuniformity correction (NUC) algorithm based on Kalman Filter is adopted and
improved by using piecewise method of detector response, which is on the premise that response factors of each detector
in infrared focal plane arrays (IRFPA) are regarded as random state variables modeled by a discrete-time Gauss-Markov
process. The effect of nonlinear response of the IRFPA detectors to the NUC accuracy can be solved by our algorithm
effectively. The algorithm can also improve the NUC result, especially, in the condition of wide dynamic range
application. The experimental results show that our algorithm gives good correction precision and performance.
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This paper proposes a global threshold selection method to do infrared image segmentation, which uses both gray-level distribution and spatial information, namely, two-dimensional OTSU method (2D OTSU). It often gets better anti-noise performance. What's more, taking consideration of the complexity of its computation, we introduce a new heuristic optimization algorithm, called the particle swarm optimization (PSO) algorithm to search the result. So an algorithm for PSO-based 2D Otsu segmentation is proposed. The experiments of segmentation the infrared images are illustrated to show that the proposed method can get ideal segmentation result with less computation cost.
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Estimates of vegetation water content are of great interest for assessing vegetation water status in agriculture and forestry, and have been used for drought assessment. This study focuses on the retrieval of foliar water content with hyperspectral data at canopy level. The hyperspectral image used in this study was acquired by the airborne operative modular imaging spectrometer (OMIS) at Demonstration Site for Precision Agriculture in Xiaotangshan area, Beijing, on April 26th, 2001. 40 image spectra were extracted to correspond to the quasi-synchronous meansurements of foliar water content (FWC). The image spectra of winter wheat were utilized to validate the sensitivity of the existing and novel water indices and parameters of three water absorption features in NIR and SWIR regions. Correlation analysis showed that, NDWI(860,1241) and NDWI(860,1200) both had significant linear relationships with FWC (R2 were 0.4124 and 0.4042 respectively). Red edge position (REP) could reflect indirectly the variations of wheat FWC to some extent. Significant linear relationships were also found between WI(820,1600) and FWC, and between WI(900,1200) and FWC, while no relationship was shown between the traditional WI(900,970) and FWC. The derived depth of water absorption centered around 2078nm, namely AD2078, had the highest linear correlation with FWC (R2 is 0.4551) , much higher than those parameters derived from the two water absorption around 1175 and 1409. In the end, AD2078 was applied to OMIS image to map the foliar water content. The value range of the inverted foliar water content ranged from 69.39 to 78.35%, which was quite close to that of the field measurements (70.72-78.12%). The distribution of the FWC map was quite consistent with growth status of winter wheat.
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Beijing-1 satellite is a DMC+4 microsatellite which will be used in land use, city planning field. It provides both multispectral (MS) and panchromatic (PAN) data with spatial resolutions of 32m and 4m, respectively. Fusion is used to produce high-resolution multispectral images from a PAN image and low-resolution MS images. In this paper, beijing-1 PAN image and MS images of Chengdu are fused by both COS method and wavelet transform method. Several experiments are taken to test the properties of fusion result. Experiment results show that wavelet based fusion method provided satisfied results with better quality than the other methods in both spatial and spectral domains.
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Coherent Target Analysis (CTA) method derived for the (Interferometric SAR) InSAR data processing has developed as one of the critical techniques in the field of surveying subtle land deformation. This paper discusses the principles and characteristics of CTA procedure, which can remove the errors caused by temporal and spatial decorrelation and atmospheric effects. The paper presents the validation of characteristics of CTA applied in city subsidence in Shanghai test site, where has been affected by subsidence for more than eighty years. The 32 scenes from the ERS-1/2 satellites covering the test area between 1992 and 2000 were chosen and processed. Based on the theory of CTA, we can get the map of land subsidence of Shanghai. Quality assessment between the velocity fields derived of CTA and that of leveling data observed on benchmarks within the same periods is then discussed. The comparison with ground leveling data shows the derived result from CTA approach is consistent with leveling data. The further investigation of CTA approach in Shanghai will be continued to improve the accuracy and robustness.
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An iterative blind deconvolution algorithm for degrade image is presented in this paper. The algorithm includes two steps, namely, the estimation of the point spread function of degrade image and the restoration using estimated point spread function. Two different Hopfield neural networks are built for realizing the two steps. An iterative procedure is used to control the restoration process. The simulation results indicate that the method is effective for blind deconvolution with high convergence speed.
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A non-parametric random field model by Ashikhmin algorithm is introduced to synthesize the infrared textures in detail. The
non-parametric model provides the spatial structure of the infrared texture, which makes it more similar with the infrared textures taken by infrared camera. Then a conversion of texture to temperature field is given to yield a temperature distribution by given a group of temperature parameters. The Planck's radiation law is applied to obtain the emittance distribution sample, called physical infrared texture which is always used in the hardware-in-the-loop systems, from the temperature field. And a method to generate the appearance infrared texture is developed by applied the effect of infrared imaging system. Finally, some examples of infrared textures synthesized by Ashikhmin algorithm are presented in the paper. The simulation results show that the non-parametric model is more available, more efficient, more suitable and more easily controlled to synthesize the infrared textures in the infrared scenes simulations.
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With the availability of multi-sensor data in the field of remote sensing, sensor fusion has emerged as a promising research area. This study presents a simple spectral preservation fusion approach based on band ratio and weighted combination. It injects spatial features into multi-spectral images to improve the spatial information, and adjusts the ratio between the high spatial resolution image and the multi-spectral image with a weight factor to reduce the color distortion. This method is applied to merge SPOT and LANDSAT (TM) images. Visual and statistical analysis prove that the technique presented here is clearly better than the conventional image fusion techniques for preserving the spectral properties with the spatial detail improved synchronously.
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This paper presents a novel fusion approach using PCA merger based on multiscale decomposition (MSD), combined with region segmentation and support vector machine (SVM), the result is a high spatial resolution multispectral image from a high resolution panchromatic (Pan) image and low resolution multispectral (Ms) images. Principal components analysis (PCA) fusion technique is one of typical fusion methods, and PCA merger based on MSD had been proposed which can obtain better performance. As we know that, in pixel fusion level, the original images are fused as internal region regardless of the contents of images, but in this paper, we perform region segmentation after MSD, because the homogeneous regions have similar features such as color, texture and intensity. Traditionally, various fusion rules can be applied after MSD according to different conditions, however, the crucial problem is which fusion rule should be adopted under given condition, hence we use the SVM to combine the most fusion rules so that can avoid some drawbacks using single fusion rule. To validate our approach, we compare it with several typical fusion approaches, and the best result is obtained using our approach.
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Laser radar can simultaneously produce the range image and the intensity image, and it can directly collect rich information of target. Compared with the other sensors, such as infrared or radar, laser radar can enhance the recognition rate and the precision of target aimed point. When laser radar vertically detects the objects on the plane ground, the correlation filters with in-plane rotation invariance are usually used to solving the problem of the target recognition. Traditional correlation filters are still improved on the aspect of recognition rate. In the paper, through deducing the relationship between support vector machine (SVM) and correlation principle in the signal processing, a new correlation filter, named linear SVM correlation filter (LSCF) that has the properties of SVM, is proposed. The real images of laser radar are used as the training and testing samples. The experiments state that the filter has good recognition attributes, such as stable correlation output and high recognition rate. LSCF is suitable to be the recognition algorithm of the imaging laser radar.
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This paper presents a fusion method for multi-focus images to produce a new clear image for the scene. First we analyze
the defocused image formation process and obtain a set of ill-posed equations. By imposing some constraints, they
become well-posed and the fusion task is converted to a simple optimization problem. Then the optimization problem,
which minimizes the gradient difference and intensity difference with respect to the objective gradient field and intensity
constraints, is equivalent to the optimal fusion method for multispectral image fusion. So we construct the objective
gradient field and the intensity constraints by clarity analysis and model change respectively and obtain the minimization
result by the iterative optimization steps of optimal fusion. At last, the experiments convincingly demonstrated that the
proposed method has better tolerance to misalignment and noises than wavelet fusion.
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A direct change detection method that utilizes the dynamic Bayesian network (DBNs) is proposed to detect buried
objects. The DBNs uses the time series dynamic data to produce credible probabilistic reasoning, and is developed to
utilize the IR images obtained by different band and temporal. The proposed method offers a way to change detection
analysis from the static viewpoint to the dynamic viewpoint, which can input and deal with more than two multi-temporal
images simultaneously which are featured by multi-band. The origin of thermal contrast in infrared imaging
between the buried objects and background is illuminated on the theory of infrared radiation. The differences of
temperature can be captured by multi-temporal and multi-band infrared images. The IR images of the regions of interest
(ROI) acquired at three different times as inputs to detect buried objects using multi-temporal direct change detection
based on physical principle of infrared imaging. The experimental results indicate that the change detection method
based on DBNs is an effective to buried objects detection.
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This paper deals with subpixel accuracy Synthetic Aperture Radar (SAR) image registration which also satisfies
real-time demand of SAR image processing. Because of the presence of speckle noise in SAR image, improved region
detection method are carried on in reference image and sensed image respectively firstly. Then each region is represented
by a set of invariant moments and chain coding of the region boundary. Correspondence between the regions in the
reference image and sensed image is established by the improved regions matching criterion which proposed by us. The
centers of gravity and the corners on the region boundary are the potential control points. Correspondence between the
control points is established in the feature space, using the principle of minimum distance classifier. After finding
enough and right matched control points, interpolation and estimation of transformation parameters using least squares
method are executed finally.
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Since Chavez proposed the highpass filtering procedure to fuse multispectral and panchromatic images, several fusion
methods have been developed based on the same principle: to extract from the panchromatic image spatial detail
information to later inject it into the multispectral one. In this paper, we present new fusion alternatives based on the
same concept, using the multiresolution contourlet decomposition to execute the detail extraction phase and the
generalized intensity-hue-saturation (GIHS) and principal component analysis (PCA) procedures to inject the spatial
detail of the panchromatic image into the multispectral one. Experimental results show the new fusion method have
better performance than GIHS, PCA, wavelet and the method of improved GIHS and PCA mergers based on wavelet
decomposition.
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In passive millimeter wave (PMMW) imaging, antenna size limitations lead to the problem of poor resolution of
acquired image. Thus efficient post-processing is necessary to achieve resolution improvement. In this paper, we present
an Adaptive Porjected Landweber super-resolution algorithm that attempts to leverage the strong points of both
Landweber iteration and projection-based adjustments. In the algorithm, we implement the Landweber iterations as the
main image restoration scheme and include a projection-based adjustment for enforcing constraints after each Landweber
iteration is completed. Furthermore, the algorithm updates the parameter adaptively at each iteration. From experiments,
we find that the Adaptive Projected Landweber superresolution algorithm obtains better results and has lower mean
square error (MSE) and produces sharper images. These constraints and adaptive characters speed up the convergence of
the Landweber algorithm.
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Recognition of targets in high-resolution synthetic aperture radar(SAR) imagery is a challenging problem in practice.
Features of military targets in SAR imagery play important roles in SAR ATR. Robust feature extraction of these targets
is difficult due to extended operating conditions such as obscuration, articulation, varied configurations and a host of
camouflage. In this paper, a new method based on wiener filtering reconstruction in multi-wavelet domain to enhance
target region feature is presented. It uses a wavelet thresholding estimate as a mean to design a wavelet domain Wiener
filter employing two different bases of wavelet, one for the thresholding step and another for the filter application. The
experimental results demonstrate that the method can improve the target regional feature, can augment statistical
separability between targets and clutter, and benefits to speckle suppression using publicly released SAR data from
DARPA's MSTAR program.
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A novel visualization method of terahertz time-domain spectroscopy (THz-TDS) image is presented, which is based on
principal component analysis (PCA) technique. The proposed method include three processing steps: firstly, the THz-
TDS image is preprocessed using a spatial vector filtering technique to denoise. Secondly, the THz-TDS image is
transformed from spatio-temporal domain to spatio-spectral domain, and the transformed image can be viewed as a
multispectral image whose spectral dimensionality D is equal to the sampled number of THz-TDS pulse at each pixel.
Thirdly, each of spectrum vector at a pixel is viewed as a point in D dimensional space, the covariance matrix of pixels
can be computed, and then three eigenvectors corresponding to the first 3 largest eigenvalues are found by PCA
technique. the THz-TDS image is projected along these three eigenvectors. By normalizing these 3 principal component
images and mapping them into the RGB space, we can get a synthetic color image as a visualization result of the THz-
TDS image. Due to vector-based dimensionality reduction, the proposed method can provide more visual information of
the THz-TDS image than scalar-based visualization techniques. Finally, experimental results are provided to demonstrate
the performance of the proposed method.
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In this paper, a pixel-level image fusion algorithm based on Nonsubsampled Contourlet Transform (NSCT) has been
proposed. Compared with Contourlet Transform, NSCT is redundant, shift-invariant and more suitable for image fusion.
Each image from different sensors could be decomposed into a low frequency image and a series of high frequency
images of different directions by multi-scale NSCT. For low and high frequency images, they are fused based on local-contrast
enhancement and definition respectively. Finally, fused image is reconstructed from low and high frequency
fused images. Experiment demonstrates that NSCT could preserve edge significantly and the fusion rule based on region
segmentation performances well in local-contrast enhancement.
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A new supervised classifier based on image fusion of hyperspectral data is proposed. The technique first selects the
suitable bands as the candidates for fusion. Then, the bands based on curvelet transform are fused into several
components. The fused hyperspectral components as the extracted features are fed into the supervised classifier based on
Gaussian Mixture Model. After the estimation of the GMM with Expectation Maximization, the pixels are classified
based on the Bayesian decision rule. One requirement of the technique is that the training samples should be provided
from the hyperspectral data to be analyzed. The main merits of the new method contain tow folds. One is the novel
feature extraction based on curvelet transform which fully makes use of the spectral properties of the hyperspectral data.
The other one is the low computing complexity by reducing the data dimension significantly. Experimental result on the
real hyperspectral data demonstrate that the proposed technique is practically useful and posses encouraging advantages.
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In this paper G.Konecny model is first realized by programming to accomplish SAR image ortho-rectification, in order
to validate the feasibility of this model. Then, for calculating exterior orientation elements of satellites, this paper uses
some different solution methods such as Singular Value Decomposition (SVD), Ridge Estimate (RE), Generalized Ridge
Estimate (GRE), Linear elements and Angle elements Detaching arithmetic (LAD) and so on, and compares them in
iteration stability, convergence rate and result precision. Some primary conclusions are summarized at last.
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This paper proposes an edge-directed interpolation algorithm for infrared images. At present, the resolution of infrared
focus planar array (IFPA) is relatively low. Conventional linear interpolation schemes such as the pixels replacement, the
bilinear interpolation and the bicubic interpolation result in blurred edges and zigzag pictures. The correlation of
different edge direction was calculated at each pixel to be interpolated to detect the edge and the edge direction. There
are 13 directions in two quadrants we have chosen. Most edge can be detected in this range. Pixels at the edge are
interpolated along the edge. The non-edge pixels are bilinearly interpolated. Simulation results show that the proposed
method effectively removed the zigzag and blur at the edge caused by conventional linear interpolation. And this method
is easy to be carried out by hardware.
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Based on the nonsubsampled contourlet transform (NSCT) and two denoising models (i.e., fractional power model and
cross-scale correlation model), an efficient pre-processing algorithm for infrared image is proposed. In our algorithm, the
NSCT is used to decompose the image at different scale and orientation, and then implement pre-processing in the
frequency domain, at last reconstruct coefficients to obtain ideal infrared image. The key of the proposed algorithm is
pre-processing which includes noise removal and information enhancement. To reduce the two kinds of noises (i.e.,
Gaussian noise and shot noise) efficiently, the two models referred are applied to the NSCT coefficients respectively.
The filtered results are fused to learn from the strong points of each denoising methods to offset the weakness of each
other. Later, the denoised coefficients are classified to edges and noise and modified by a nonlinear mapping function.
Experiments carried on infrared images show that the new algorithm can reduce the Gaussian noise and shot noise
efficiently, while keeping the detail information well. Both in the objective performance index and subjective viewing
assessment, the new algorithm is superior to the DWT-based method as well as the traditional method.
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In order to reduce high dimensions of hyperspectral remote sensing image and concentrate optimal information to
reduced bands, this paper proposed a new method of feature extraction. The new method has two steps. The first step is
to reduce the high dimensions by selecting high informative and low correlative bands according to the indexes
calculated by a smart band selection method. The criterions that SBS method complied are: (1) The selected bands have
the most information; (2) The selected bands have the smallest correlation with other bands. The second step is to
decompose the selected bands by a novel second generation wavelet, predicting and updating subimages on rectangle
and quincunx grids by Neville filters, finally using variance weighting as fusion weight. A 126-band HYMAP
hyperspectral data was experimented in order to test the effect of the new method. The results showed classification
accuracy is increased by using the novel feature extraction method.
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This paper introduces a new amendment dielectric model in the microwave band concerning the principle of soil science
which can also give the information about soil texture and variety of soluble salt ions, and points out some errors of
original model at the same time. The new model is confirmed suitable for saline soil with different levels of moisture and
salinity soil samples prepared in lab. Based on the research on the dielectric properties of saline and alkali soils, this
paper also brings forward the feasibility of separating them. Contrasts to the classic dielectric models of soil, all the
parameters of the improved model have their physical meanings, and the procedures avoid neglecting principle of soil
science and optimization blindly. So the parameters can give us more information about moist saline soil. This study
sums up dielectric properties of moist soil, and adds salt-affected factor. Thus we can describe dielectric behavior of
moist saline soil more precisely. In further research it pays attention to the relationship between dielectric properties and
backscattering coefficients or polarimetric information extracted from radar image.
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Synthetic aperture radar (SAR) images are corrupted by speckle noise due to random interference of electromagnetic
waves. The speckle degrades the quality of the images and makes interpretations, analysis and classifications of SAR
images harder. Therefore, some speckle reduction is necessary prior to the processing of SAR images. In this paper, we
propose a method of speckle reduction in SAR images based on Wedgelet Furthermore, an edge-enhanced local mean
and median filter is added to smooth SAR speckle noise while preserving edges. Experiments show that this method can
significantly reduce the speckle while preserving more edge structures of the original SAR images than classical ones.
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Because the airdrome is a target on the ground, this kind of target has the complex characteristic. So we must
think about all kind of factor to design the algorithm which can meet the practice, when the algorithm of
proceeding infrared image is designed. This paper advises the track-before-detect (TBD) algorithm to process
infrared image by the practice and modern image procession technology. Our results indicate that this algorithm
can distinguish between the real target and the false target under small SNR. SNR is about 1.8.
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With the advantages of high resolution and accuracy, airborne laser scanning data are widely used in topographic
mapping. In order to generate a DTM, measurements from object features such as buildings, vehicles and vegetation
have to be classified and removed. However, the automatic extraction of bare earth from point clouds acquired by
airborne laser scanning equipment remains a problem in LIDAR data filtering nowadays. In this paper, a filter
algorithm based on wavelet analysis is proposed. Relying on the capability of detecting discontinuities of continuous
wavelet transform and the feature of multi-resolution analysis, the object points can be removed, while ground data are
preserved. In order to evaluate the performance of this approach, we applied it to the data set used in the ISPRS filter
test in 2003. 15 samples have been tested by the proposed approach. Results showed that it filtered most of the
objects like vegetation and buildings, and extracted a well defined ground model.
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"Beijing-1" microsatellite is a new earth observation satellite which China has the right of its operation, management and
controlment. It has various technological advantages. This paper explores the feasibility of using "Beijing-1"
microsatellite data to estimate vegetation fraction. In this study the Drainage basin of Miyun Reservoir is selected as the
study area, and the NDVI method for dimidiate pixel model is chosen as the study method. Through validating with
field-investigated data, which is obtained by calculating the vegetation fraction on the digital photography of each land
cover type, the average estimated accuracy of vegetation fraction of all land cover types is more than 87.5% in the study
region. The result shows that using the "Beijing-1" microsatellite data to estimate and monitor the vegetation fraction at a
large scale is feasible.
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Multi-spectral images have a high degree of spectral resolution. Through distilling the spectrum features as well as
texture features between target and background, and calculating the Mahalanobis Distance of spectrum features as well
as texture features data vector, then we can accordingly analyze the blend performance of pattern painting camouflage
quantitatively. Utilizing the generating pixel spectrum curve function by the tool of Z Profile of ENVI, the remote
sensing imaging processing system, we can draw the spectrum curve of target and typical background, and choose the
spectral bands with small data relativity and obvious spectrum value difference, combining with principal component
analysis. According to the result of spectrum analysis, we can establish the gray level co-occurrence of chosen image or
area, and get texture characteristic, then take the Mahalanobis Distance calculated by spectrum features as well as texture
features data vector between target and background of typical spectrum band images as the foundation of performance
evaluation of pattern painting camouflage.
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Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the
coherent nature of the scattering phenomenon. This paper proposed an adaptive regularized approach to reduce SAR
image speckle based on least squares support vector machines (LS-SVM). Generally, SAR images are comprised of
multiple features of different spatial scales, and there is typically a trade-off between speckle removal and detail
preservation. A natural approach to partially alleviate this problem is to use spatial adaptive regularization parameter on
the use of regularized procedure. Here, each pixel has its own associated regularization parameter in this paper, instead
of choosing a global regularization parameter. Experimental results show that our approach has a good performance on
the speckle reduction without destruction of important SAR image details.
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The fusion effect on the high-resolution remote sensing image using the traditional fusion technique such as Principal Component Analysis(PCA), is not satisfying. Considering the well-developed technique of Minimum Noise Fraction(MNF) transform and the flexible ability of Wavelet transform, a new fusion method (MNFWT) integrating MNF and Wavelet transform was studied using multi-spectral (MS) IKONOS image at 4-m spatial resolution and panchromatic (PAN) IKONOS image at 1-m resolution. Compared with PCA fusion method, MNFWT approach performs more efficiently both in improving the spatial information and preserving the spectral information.
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A method based on BRDF for topographic correction and surface reflectance estimation from Landsat TM over rugged terrain is presented. The BRDF factor is used to remove the variation of relative solar incidence angle and relative sensor viewing angle per pixel. Solar direct radiance, sky diffuse radiance and adjacent terrain reflected radiance as well as atmospheric transmittance and path radiance are analyzed in detail and calculated in per pixel using a look up table (LUT) with a digital elevation model (DEM). The method is applied to a Landsat TM imagery that covers a rugged area in Jiangxi province, China. Results show that atmospheric and topographic correction based on BRDF factor gives better surface reflectance.
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The correlator is the key signal processing equipment of a Very Lone Baseline Interferometry (VLBI) synthetic aperture telescope. It receives the mass data collected by the VLBI observatories and produces the visibility function of the target, which can be used to spacecraft position, baseline length measurement, synthesis imaging, and other scientific applications. VLBI data correlation is a task of data intensive and computation intensive. This paper presents the algorithms of two parallel software correlators under multiprocessor environments. A near real-time correlator for spacecraft tracking adopts the pipelining and thread-parallel technology, and runs on the SMP (Symmetric Multiple Processor) servers. Another high speed prototype correlator using the mixed Pthreads and MPI (Massage Passing Interface) parallel algorithm is realized on a small Beowulf cluster platform. Both correlators have the characteristic of flexible structure, scalability, and with 10-station data correlating abilities.
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In this paper we propose an approach to coherent high resolution image simulation of synthetic aperture radar (SAR) that observes natural terrain scenes. The present approach preserves the relative phase information of each individual scatter when it calculates the backscattering from terrain scene objects that composed of some elementary scatters whose scattering model are well investigated. We adopt the Bin Sort algorithm to simulate SAR imaging process; it combines the extinction, attenuation and shadowing effects of all scatters as well as multiple scattering effects of some strong scatters such as tree trunks and main branches. The fractal concept and Lindenmayor system are employed to model the realistic tree structure while other natural objects are constructed based on the ground truth data. As an example, a coherent SAR image for a computer-generated scene that includes trees, grassland, rough land and water area were simulated.
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Accurately simulating the effect of atmospheric turbulence on the laser beams to make an effective compensation will improve the imaging ability of practical synthetic aperture ladar (SAL). Based on the modeling of atmospheric turbulence with phase screens generated by Monte Carlo algorithm and power spectrum method, and using the theory of Fourier propagation, the SAL backscattered signal of assumed Gaussian beam from point objects is simulated. The results show that phase distortion in atmospheric turbulence is severe that could reduce SAL imaging ability. A corresponding method, iterative Fourier phase conjugation (IFPC) algorithm, is provided to minimize the phase aberrations, and compensation results show that this algorithm can give us a better SAL solution.
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For the requirement of fast restoration, an algorithm based on multi-scale blind deconvolution is proposed by ing the multi-scale technology based on wavelet transforms. In the condtion of without knowing the point spread function (PSF), two observed images will be used as the inputs, and the two PSFs at large scale can be estimated by using of the data of the two low-frequency subband images and by incorporating some constraint regularizations. Deblurring is made in the low-frequency subband of the two images while restraining noises and preserving details in other three high-frequency subbands. A series of experiments have been performed to verify the effectiveness of the proposed algorithm.
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Many ETM (Enhanced Thematic Mapper) digital image processing methods such as ratio and principal component analysis (PCA) have been developed in exploration with spectral feature reflected in ETM data. Unfortunately, by our knowledge, there is no clear idea in ascertainment on abnormality component in PCA and no quantitative scale to classify abnormality for alteration. In this study, to improve exploration efficiency, the rules of ascertainment on abnormality component and classification on abnormality for alteration were established. PCA with ETM bands 1, 4, 5, 7 and ETM bands 1, 3, 4, 5 with interferential factors masked for OH- alteration and Fe2+ and Fe3+ alteration respectively were conducted. Meanwhile, the rules for ascertainment on alteration abnormality by PCA for OH- or Fe2+ and Fe3+ alteration were well established by the contribution of their diagnostic spectral bands. That is, the abnormity component of principal components (PCs) for OH- alteration can be ascertained by obtaining positive contribution from ETM band 5 but negative from ETM band 7, while that of Fe2+ and Fe3+ alteration obtained positive contribution from ETM band 3 but negative from ETM bands 1, 4 and 5. Alteration grades were delineated by the standard deviation σ and average μof pixel value of abnormality component which coincides with probability density function. Accordingly, drill holes conducted in potential areas of alteration, a famous mineralization belt in China, Northwestern Yunnan, also reveals that alteration-related information extracted from ETM data is practical and effective in mineral application.
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The original principle of image fusion based on pixel level is using the spatial and spectral information from different remotely sensed data to generate a new image. So, the fusion method with high preservation is the main issue. In this paper, modified Brovey transform (MBT) has been proposed based on the principle of the Brovey transform model. Three fusion methods of MBT, wavelet transform (WT) and smoothing filter-based intensity modulation (SFIM), which can merge each band images directly, are applied to respectively merge multi-spectral data with panchromatic image of ETM+ and QB sensors. The qualitative evaluation and quantitative computation analysis show that MBT has the highest high frequency information preservation and SFIM model enjoys the best low frequency information preservation, and both of them can be used to deal with large numbers of images fusion for their fast computation capability. The WT has a suboptimal spectral maintenance and the lowest high spatial frequency gain among the mentioned three data fusion algorithms, and it takes more time to finish the progress of data fusion.
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Canopy nitrogen content has strong relationship with net primary productivity, litter nitrogen and nitrogen mineralization rate, so the estimation of nitrogen content can provide valuable understanding of large-scale terrestrial carbon and nitrogen cycle. Hyperspectral remote sensing technology demonstrates the capacity for accurate biochemical component estimation of vegetation. This paper employed Hyperion hyperspectral data acquired over Xishuangbanna tropical area in Yunnan province, China to estimate nitrogen content based on normalized band depth (BNC) method. Hyperion data geometric and radiometric corrections were first made, and then Hyperion reflectance of 56 samples in 35 plots was extracted. Continuum removal was applied to the selected absorption features related to nitrogen. The BNC of 56 samples were calculated. Relationships between BNC values in Hyperion image and in situ field measured nitrogen content were analyzed using stepwise multiple linear regression. Results showed that central wavelengths in the model predicting nitrogen were 650.67nm, 2213.93nm, 2173.53nm and 671.02nm, and coefficient of determination (R2) was 0.505. Bands 650.67nm and 671.02nm coincided with chlorophyll absorption features highly related to nitrogen; 2213nm and 2173nm corresponded to protein and nitrogen absorption features. Correlation analysis showed that the biggest correlation coefficient between nitrogen and BNC was -0.573, which was at 650.67nm.
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Research on the generation of multi-spectral scene image for ocean environment is helpful for flight testing, mission routes planning, target recognition, military affairs, etc. Firstly, base on the spectrum equation of ocean wave movement, we use local evolvement of cellular automata, and build displacement rules for ocean wave with space and time. After calculating multispectral radiation of ocean wave, we generate the dynamic multispectral ocean wave. Then we establish the geometrical models of littoral environment, including terrain, coast, island, etc. Further, we calculate the radiance of different spectrum for ocean wave and ocean environment including visual and infrared, and quickly generate the multi-spectral ocean scene. Finally, after using some rendering techniques, we generate different realistic multispectral littoral environment scenes under different conditions in near real time.
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This paper presents a combined, two-block framework for unsupervised image segmentation, which is capable of leveraging the best qualities of the watershed transform and MRF models and taking advantage of multi-cue information. The first block extracts various features that respond to different cues of the image and generates their gradient images. Then the obtained gradient images are combined to form a single-valued gradient surface, whose watershed transform provides over-segmented, but homogeneous image regions. The second block of our algorithm groups together these primitive regions into meaningful object based on an improved MRF model. The proposed algorithm is compared with other traditional methods in segmentation of Brodatz texture mosaics and real multi-spectral image. The satisfying experimental results demonstrate the better performance of our new framework.
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Contemporary and future ultraspectral sounders represent significant technical advancement for environmental and meteorological prediction and monitoring. Given their large volume of spectral observations, the use of robust data compression techniques will be beneficial to data transmission and storage. The retrieval of geophysical parameters from ultraspectral sounder observation via the radiative transfer equation is a mathematically ill-posed problem. Lossless compression of ultraspectral sounder data is desired by the science community to avoid potential retrieval degradation. For NOAA's future geostationary weather satellites, the data is managed to be transmitted down to the ground within the bandwidth capabilities of the satellite transmitter and ground station receiving system. The data is then compressed at the ground station for distribution to the user community, as is traditionally performed with the GOES data via satellite rebroadcast. In this paper we investigate a lossless compression method with fast precomputed vector quantization (FPVQ) and reversible variable-length coding (RVLC). The FPVQ produces high compression gain for ground operation while RVLC affords better detection of bit errors remaining after channel decoding due to synchronization losses over a noisy channel. The FPVQ-RVLC compression method provides a good tool for satellite rebroadcast of ultraspectral sounder data.
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This paper aims to describe an improved method for integrating the SAR features into multispectral(MS) images based on the second generation curvelet transform. Curvelet transform(CT),as a special member of the emerging family of multiscale geometric transform, overcomes inherent limitation of traditional multiscale representation and owns very high directional sensitivity and anisotropy. It represents edges and singularities along curves much more efficiently than the traditional wavelet transform. To get more information in fusion image, the curvelet transform is introduced. The proposal of the second generation curvelet theory makes it understood and implemented more easily. SAR image has higher spatial resolution, but MS images have more spectral information. In order to get maximal integration of SAR features and the maximal preservation of the spectral content, two kinds of images may be implemented by different scales curvelet decomposition. And these decomposed curvelet coefficients can be processed according to certain fusion regular. Then detail information of SAR and approximate information of MS will be extracted by inverse curvelet transform respectively. At last fused image is obtained by injecting SAR's detail information into the MS's approximate information. Landsat TM and SAR images covering a region of sanshui in Guangdong province are used to evaluate the effect of the proposed method and some other fusion methods in terms of spectral preservation and spatial resolution improvement. The results show that the proposed method can provide richer information in the spatial and spectral domain.
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Traditional image matting approaches requires user interaction. This paper proposes an automatic framework for natural image matting. The method seamlessly incorporates image matting with the top-down process of segmentation by weighted aggregation to get a rich and multi-scale grapy pyramid representation of the input image. Using the coupling between aggregates in the graph pyramid, the region for matting is detected adaptively and automatically. Meanwhile, foreground and background regions are determined with state variables. An energy function is constructed to represent the similarity and smoothness properties of a matte and is iteratively optimized. Under the automatic matting framework, color sampling is more accurate than existing methods since multi-scale measurements such as intensity and texture are fully considered. Experiments show that the proposed automatic method is more efficient to extract high quality matte even for difficult images in which foreground and background have very similar colors. Another attractive feature of the method is that it can extract mattes for multi-objects at one computing time.
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The Himalayan region comprises extensive snow cover areas having various degrees of glaciations that act as a huge fresh water reservoir. Mapping the extent of these snow covered areas and deciphering the characteristics of these snow pack help in the estimation of snow melt runoff. This study involves the mapping of snow cover area around Gangotri glacier using remote sensing data analysis. The data products include IRS LISS-III, IRS P6 AWiFS and TERRA MODIS. The methodology involves conversion of digital numbers (DN) into actual reflectance values. Snow index derived on the basis of reflectance value will be more accurate because different objects having the same DN value may likely to correspond to different reflectance and therefore the use of reflectance improves the identification of objects. The final Snow cover area (SCA) maps are prepared based on the Normalised Difference Snow Index (NDSI) values. The Snow cover maps were classified into two classes: Snow cover and Non-snow cover. The threshold value greater than 0.4 is used in distinguishing snow from soil, rock and cloud. In this investigation it has been found out that from the remote sensing data products used, LISS-III and AWiFS sensors are far superior compared to MODIS and MERIS. Hence the snow cover maps for different months of 2003 and 2004 were produced using only LISS-III and AWiFS data products. The analysis of these maps indicates that the snowfall in 2002-03 season was higher than in 2003-04. The melting of snow started as early as March in 2004, thus resulting in depletion of snow cover extent. These observations could be corroborated with the field weather data collected by Automatic Weather Station (AWS).
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With extensive application of image change detection, the author researches kinds of algorithms of image change detection in detail. Based on analysis of several typical change detection algorithms, an improved algorithm of change detection based on independence component analysis (ICA) is proposed; simultaneously, an integrated change detection scheme is presented. Taking remote sensing image with complex background for example, simulation results show that the improved algorithm and proposed scheme have advantage over current algorithm.
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A hybrid method integrated wavelet spectral feature with total least square algorithm for improving abundance estimation of hyper-spectral mixture pixels is proposed. The method uses the wavelet transform as a pre-processing step for the spectral feature extraction to decrease the within end-member variability, and then utilizes total least square (TLS) algorithm to capture the spectral variations between end-members. The hybrid method can take both technique advantages to reduce the impact of spectral variations with different format. Consequently, the approach provides a potential ability to reduce and tackle within end-member variation inherent in real mixture pixels, and hence to improve abundance estimation. Experiment of simulating mixture spectral data is conducted to validate the procedures, and the results demonstrate that the proposed method can reduce the abundance estimation deviation over 20% on average in the case of spectral end-member variations, as compared to that of the original hyper-spectral signals with least square estimation approach does. Comparisons with the decomposition of wavelet based features (DWT) and total least square have also been implemented, and the experiment shows the hybrid method can also improve the abundance estimation by 5%-10% than those of DWT and TLS do in terms of average RMSE.
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Based on sparse representation, a new novel SAR image despeckling algorithm (SR) associate with the feature of SAR image is presented. The experimental results show that the proposed algorithm has capabilities of denoising and preserving edge and texture features. Moreover, it outperforms the four traditional denoising methods in speckle smoothing, edge and texture preservation and visual assessment.
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MQ arithmetic coder has been adopted to achieve entropy coding in the latest image compression standard JPEG2000, which is a bit-level operation with intensive branch and feedback thus becomes a serious bottleneck of high speed JPEG2000. In this paper, an efficient implementation scheme for MQ coder was proposed, in which the renormalization process with BYTEOUT was performed in batch fashion instead of gradual iteration as introduced in JPEG2000. Experimental results have proved the validity of this method in decreasing computation complexity.
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In this paper, a new method for edge detection in multispectral images is presented. The method is based on the assumption that edges correspond to the boundary that separates two adjacent homogeneous regions, and characterization of edges are conducted by evaluating the difference between the two adjacent neighborhood regions to derive edge intensity and orientation. To demonstrate the efficiency of the proposed approach, we carried out computer experiments on multiple-band images and comparison with other methods. Experiments on a variety of images have shown that it is consistent and reliable tool to detecting edges of multispectral images. The proposed method can detect the edges successfully and can resolve to thick edges.
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Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. Speckle noise of SAR affects image quality and image interpretation seriously. To alleviate deleterious effects of speckle, various ways have been devised to suppress it. An ideal algorithm should smooth the speckle without blurring edges and fine details. But most classical algorithms cannot satisfy these two demands very well. Due to the property of SAR images speckles is multiplicative noise, it difficult to estimate the variance of the high-frequency subband coefficients. Most classical approaches such as wavelet thresholding or shrinkage scheme of Donoho and Johnstone are not suitable for SAR images speckle noise removal. In this paper, a novel approach to SAR image speckle reduction is presented, which is based on second generation bandelets and a kernel-based possibilistic C-means clustering algorithm (BKPCM).
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Hyperspectral remote sensing can provide tens, even hundreds of spectral bands imagery, which helps us detect the diagnostical spectral characteristics of detected objects. However, there is relatively high correlation between different bands and much redundancy in hyperspectral data sets. Therefore, one of the most important procedures before application is to select optimal bands for extracting information from hyperspectral data effectively. In this paper, we first introduce the characteristics of EO-1/Hyperion, and apply several important pre-processing procedures to Hyperion L1R data, such as radiometric calibration, destriping, smile correction etc. Then we apply spectrum reconstruction approach to feature selection, which uses several basis functions and corresponding spectral intervals to describe the spectrum extracted from Hyperion hyperspectral data sets in Subei region, China. The feature selection method based on spectrum reconstruction is incrementally adding bands to the initial bands, followed by adjustment of band widths and locations. At last, we aggregate several Hyperion bands into a new simulated band in each interval and apply Maximum Likelihood Classification (MLC) method to it. The overall accuracy of classification is 92% compared with in situ measurement, which supports the validity of this feature selection method.
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Recently, many researchers have shown interests in anisotropic diffusion methods in image processing. There are two key problems on image restoration techniques based on anisotropic diffusion: one is to build stable diffusion coefficients; the other is to select the optimal stopping time for iterative diffusion process. In this paper, a time-dependent robust anisotropic diffusion method is proposed. The new method is a robust anisotropic diffusion with incorporated time dependent cooling process, and the gradient threshold is the monotonic decreasing function of the time. Thus, the parameters settings and the estimate of the optimal stopping time can be easily resolved. Meanwhile, the proposed method can lessen the over-smooth in image edge features. In order to extent the applicability of the proposed method, we extent the model to the vector-valued form and present time-dependent multispectral robust anisotropic diffusion. Experimental results, performed on several gray images, color images and multispectral remote sensed images, have shown that the time-dependent robust anisotropic diffusion methods can effectively smooth out noise while preserving edge features.
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Penumbral imaging is a kind of technique which uses the facts that spatial information can be recovered from the shadow or penumbra that an unknown source casts through a simple large circular aperture. The technique is based on a linear deconvolution. As we known that the information of the penumbral image is only contained in the penumbra (the edges of the image), so according to that principle, we proposed a two-step method for decoding penumbral images in this paper. First, an edgy-emphasizing algorithm using a band filter is applied to extract the penumbras (the image edges) in noisy penumbral images; then, followed by conventional linear deconvolution of only the penumbral edges. The simulation results show that the reconstructed image is dramatically improved in comparison to that with the conventional noise-removing filters, and the proposed method is also applied to real experimental x-ray imaging.
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Iris location is an important part in an iris recognition system. Its precision and speed influence the performance of the whole system. To overcome the drawbacks of some popular algorithms with respect to these aspects, this paper proposes a novel approach which reduces the effects of eyelash occlusion and boundary blurring, two major affected factors. With this approach, the inner boundary is quickly located by searching a coarse center and the outer one by image converting, enhancing, and differentiating. The proposed approach is compared with two commonly used ones by experimental results on the CASIA database.
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A satisfied deformable object simulation should be general, accurate, efficient and stable. Explicit, implicit and semi-implicit integration methods have contributed to large performance enhancements in the field of deformable simulation. Cloth is the most representative deformable object. In this paper, we propose an improved embedded Runge-Kutta method to solve the deformable simulation that takes cloth for example based on classical spring-mass model. Traditional embedded Runge-Kutta methods generally apply some optimized coefficients to solve ordinary differential equations of deformable object simulation. Most of them tend to concentrate on the efficiency of the simulation process, and not the fidelity of the simulation result. We investigate and determine the extent to which the overall quality must be compromised in order for the stable conditions to be satisfied. The improved Runge-Kutta method proposed in our paper incorporates the fixed time step and adaptive time step in solving cloth motion equations to achieve a controllable error evaluation. Compared with the other Runge-Kutta methods, the proposed method has some advantages for cloth simulation: controllable error evaluation without extra computations, excellent efficiency, good stability and satisfied precision. Experiment demonstrates that the method improves the simulation efficiency and is considerable practicable.
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