The Experts below are selected from a list of 15537 Experts worldwide ranked by ideXlab platform
Antonio Plaza - One of the best experts on this subject based on the ideXlab platform.
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Endmember Estimation with Maximum Distance Analysis
Remote Sensing, 2021Co-Authors: Xuanwen Tao, Mercedes E. Paoletti, Juan M. Haut, Peng Ren, Javier Plaza, Antonio PlazaAbstract:Endmember estimation plays a key role in hyperspectral image unmixing, often requiring an estimation of the number of Endmembers and extracting Endmembers. However, most of the existing extraction algorithms require prior knowledge regarding the number of Endmembers, being a critical process during unmixing. To bridge this, a new maximum distance analysis (MDA) method is proposed that simultaneously estimates the number and spectral signatures of Endmembers without any prior information on the experimental data containing pure pixel spectral signatures and no noise, being based on the assumption that Endmembers form a simplex with the greatest volume over all pixel combinations. The simplex includes the farthest pixel point from the coordinate origin in the spectral space, which implies that: (1) the farthest pixel point from any other pixel point must be an endmember, (2) the farthest pixel point from any line must be an endmember, and (3) the farthest pixel point from any plane (or affine hull) must be an endmember. Under this scenario, the farthest pixel point from the coordinate origin is the first endmember, being used to create the aforementioned point, line, plane, and affine hull. The remaining Endmembers are extracted by repetitively searching for the pixel points that satisfy the above three assumptions. In addition to behaving as an endmember estimation algorithm by itself, the MDA method can co-operate with existing endmember extraction techniques without the pure pixel assumption via generalizing them into more effective schemes. The conducted experiments validate the effectiveness and efficiency of our method on synthetic and real data.
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simultaneously counting and extracting Endmembers in a hyperspectral image based on divergent subsets
IEEE Transactions on Geoscience and Remote Sensing, 2020Co-Authors: Xuanwen Tao, Antonio Plaza, Tingwei Cui, Peng RenAbstract:Most existing endmember extraction techniques require prior knowledge about the number of Endmembers in a hyperspectral image. The number of Endmembers is normally estimated by a separate procedure, whose accuracy has a large influence on the endmember extraction performance. In order to bridge the two seemingly independent but, in fact, highly correlated procedures, we develop a new endmember estimation strategy that simultaneously counts and extracts Endmembers. We consider a hyperspectral image as a hyperspectral pixel set and define the subset of pixels that are most different from one another as the divergent subset (DS) of the hyperspectral pixel set. The DS is characterized by the condition that any additional pixel would increase the likeness within the DS and, thus, reduce its divergent degree. We use the DS as the endmember set, with the number of Endmembers being the subset cardinality. To render a practical computation scheme for identifying the DS, we reformulate it in terms of a quadratic optimization problem with a numerical solution. In addition to operating as an endmember estimation algorithm by itself, the DS method can also co-operate with existing endmember extraction techniques by transforming them into a novel and more effective schemes. Experimental results validate the effectiveness of the DS methodology in simultaneously counting and extracting Endmembers not only as an individual algorithm but also as a foundation algorithm for improving existing methods. Our full code is released for public evaluation. 1 1 https://github.com/xuanwentao/DivergentSubset
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Normal Endmember Spectral Unmixing Method for Hyperspectral Imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015Co-Authors: Lina Zhuang, Lianru Gao, Bing Zhang, Antonio PlazaAbstract:The normal compositional model (NCM) has been introduced to characterize mixed pixels in hyperspectral images, particularly when endmember variability needs to be considered in the unmixing process. Each pixel is modeled as a linear combination of Endmembers, which are treated as Gaussian random variables in order to capture such spectral variability. Since the combination coefficients (i.e., abundances) and the Endmembers are unknown variables at the same time in the NCM, the parameter estimation is more difficult in comparison with conventional approaches. In order to address this issue, we propose a new Bayesian method, termed normal endmember spectral unmixing (NESU), for improved parameter estimation in this context. It considers the Endmembers as known variables (resulting from the extraction of endmember bundles), then performs optimal estimations of the remaining unknown parameters, i.e., the abundances, using Bayesian inference. The particle swarm optimization (PSO) technique is adopted to estimate the optimal values of abundances according to their posterior probabilities. The performance of the proposed algorithm is evaluated using both synthetic and real hyperspectral data. The obtained results demonstrate that the proposed method leads to significant improvements in terms of unmixing accuracies.
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Region-Based Spatial Preprocessing for Endmember Extraction and Spectral Unmixing
IEEE Geoscience and Remote Sensing Letters, 2011Co-Authors: Gabriel Martin, Antonio PlazaAbstract:Linear spectral unmixing is an important task in remotely sensed hyperspectral data exploitation. This approach first identifies a collection of spectrally pure constituent spectra, called Endmembers, and then expresses the measured spectrum of each mixed pixel as a combination of Endmembers weighted by fractions or abundances that indicate the proportion of each endmember in the pixel. Over the last decade, several algorithms have been developed for automatic extraction of spectral Endmembers from hyperspectral data, with many of them relying exclusively on the spectral information. In this letter, we develop a novel unsupervised spatial preprocessing (SPP) module which adopts a region-based approach for the characterization of each endmember class prior to endmember identification using spectral information. The proposed approach can be combined with any spectral-based endmember extraction technique. Our method is validated using both synthetic scenes constructed using fractals and a real hyperspectral data set collected by NASA's Airborne Visible Infrared Imaging Spectrometer over the Cuprite Mining District in Nevada and further compared with previous efforts in the same direction such as the spatial-spectral endmember extraction, automatic morphological endmember extraction, or SPP methods.
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Spatial Preprocessing for Endmember Extraction
IEEE Transactions on Geoscience and Remote Sensing, 2009Co-Authors: Maciel Zortea, Antonio PlazaAbstract:Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a remotely sensed hyperspectral scene. These pure signatures are then used to decompose the scene into abundance fractions by means of a spectral unmixing algorithm. Most techniques available in the endmember extraction literature rely on exploiting the spectral properties of the data alone. As a result, the search for Endmembers in a scene is conducted by treating the data as a collection of spectral measurements with no spatial arrangement. In this paper, we propose a novel strategy to incorporate spatial information into the traditional spectral-based endmember search process. Specifically, we propose to estimate, for each pixel vector, a scalar spatially derived factor that relates to the spectral similarity of pixels lying within a certain spatial neighborhood. This scalar value is then used to weigh the importance of the spectral information associated to each pixel in terms of its spatial context. Two key aspects of the proposed methodology are given as follows: 1) No modification of existing image spectral-based endmember extraction methods is necessary in order to apply the proposed approach. 2) The proposed preprocessing method enhances the search for image spectral Endmembers in spatially homogeneous areas. Our experimental results, which were obtained using both synthetic and real hyperspectral data sets, indicate that the spectral Endmembers obtained after spatial preprocessing can be used to accurately model the original hyperspectral scene using a linear mixture model. The proposed approach is suitable for jointly combining spectral and spatial information when searching for image-derived Endmembers in highly representative hyperspectral image data sets.
Derek Rogge - One of the best experts on this subject based on the ideXlab platform.
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Incorporating band selection in the spatial selection of spectral Endmembers
International Journal of Applied Earth Observation and Geoinformation, 2020Co-Authors: Yaqian Long, Benoit Rivard, Derek RoggeAbstract:Abstract The impact of band selection on endmember selection is seldom explored in the analysis of hyperspectral imagery. This study incorporates the N-dimensional Spectral Solid Angle (NSSA) band selection tool into the Spectral-Spatial Endmember Extraction (SSEE) tool to determine a band set that can be used to better define Endmembers classes used in spectral mixture analysis. The incorporation aims to define a band set that improves the spectral contrast between Endmembers at each step of the spatial-spectral endmember search and ultimately captures key features for discriminating spectrally similar materials. The proposed method (NSSA-SSEE) was evaluated for lithological mapping using a hyperspectral image encompassing a range of spectrally similar mafic and ultramafic rock units. The band selected by NSSA-SSEE showed a good agreement with known features of scene components identified by experts. Results showed an improvement in the selection of detailed Endmembers, Endmembers that are similar and that can be significant for mapping. The incorporation of NSSA into SSEE was feasible because both methods are well suited for this process. NSSA is one of the few methods of band selection that is suitable for the analysis of a small number of Endmembers and SSEE provides such endmember sets via spatial subsetting. The automated NSSA-SSEE approach can reduce the need for field-based information to guide the feature selection process.
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WHISPERS - Spatial constraints on endmember extraction and optimization of per-pixel endmember sets for spectral unmixing
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009Co-Authors: B. Rivard, Derek Rogge, Jilu Feng, J. ZhangAbstract:Fractional abundances predicted for a given pixel using spectral mixture analysis (SMA) are most accurate when only the spectral Endmembers that comprise it are used, with larger errors occurring if inappropriate Endmembers are included in the mixing process. Thus, in order to produce accurate results from spectral mixture analysis it is necessary to acquire representative endmember spectra of all image components and unmix each pixel using the appropriate endmember set for each pixel. In this paper we present an image endmember extraction algorithm which integrates spatial constraints in the search process and a spectral mixture algorithm designed to optimize the endmember set on a per-pixel basis. Implications on fractional abundances resulting from spectral unmixing analysis are then discussed.
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the successive projection algorithm spa an algorithm with a spatial constraint for the automatic search of Endmembers in hyperspectral data
Sensors, 2008Co-Authors: Jinkai Zhang, Benoit Rivard, Derek RoggeAbstract:Spectral mixing is a problem inherent to remote sensing data and results in fewimage pixel spectra representing "pure" targets. Linear spectral mixture analysis isdesigned to address this problem and it assumes that the pixel-to-pixel variability in ascene results from varying proportions of spectral Endmembers. In this paper we present adifferent endmember-search algorithm called the Successive Projection Algorithm (SPA).SPA builds on convex geometry and orthogonal projection common to other Endmembersearch algorithms by including a constraint on the spatial adjacency of endmembercandidate pixels. Consequently it can reduce the susceptibility to outlier pixels andgenerates realistic Endmembers.This is demonstrated using two case studies (AVIRISCuprite cube and Probe-1 imagery for Baffin Island) where image Endmembers can bevalidated with ground truth data. The SPA algorithm extracts Endmembers fromhyperspectral data without having to reduce the data dimensionality. It uses the spectralangle (alike IEA) and the spatial adjacency of pixels in the image to constrain the selectionof candidate pixels representing an endmember. We designed SPA based on theobservation that many targets have spatial continuity (e.g. bedrock lithologies) in imageryand thus a spatial constraint would be beneficial in the endmember search. An additionalproduct of the SPA is data describing the change of the simplex volume ratio between successive iterations during the endmember extraction. It illustrates the influence of a newendmember on the data structure, and provides information on the convergence of thealgorithm. It can provide a general guideline to constrain the total number of Endmembersin a search.
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integration of spatial spectral information for the improved extraction of Endmembers
Remote Sensing of Environment, 2007Co-Authors: Derek Rogge, Benoit Rivard, Jinkai Zhang, A Sanchez, J Harris, Jilu FengAbstract:Spectral-based image endmember extraction methods hinge on the ability to discriminate between pixels based on spectral characteristics alone. Endmembers with distinct spectral features (high spectral contrast) are easy to select, whereas those with minimal unique spectral information (low spectral contrast) are more problematic. Spectral contrast, however, is dependent on the endmember assemblage, such that as the assemblage changes so does the “relative” spectral contrast of each endmember to all other Endmembers. It is then possible for an endmember to have low spectral contrast with respect to the full image, but have high spectral contrast within a subset of the image. The spatial–spectral endmember extraction tool (SSEE) works by analyzing a scene in parts (subsets), such that we increase the spectral contrast of low contrast Endmembers, thus improving the potential for these Endmembers to be selected. The SSEE method comprises three main steps: 1) application of singular value decomposition (SVD) to determine a set of basis vectors that describe most of the spectral variance for subsets of the image; 2) projection of the full image data set onto the locally defined basis vectors to determine a set of candidate endmember pixels; and, 3) imposing spatial constraints for averaging spectrally similar Endmembers, allowing for separation of Endmembers that are spectrally similar, but spatially independent. The SSEE method is applied to two real hyperspectral data sets to demonstrate the effects of imposing spatial constraints on the selection of Endmembers. The results show that the SSEE method is an effective approach to extracting image Endmembers. Specific improvements include the extraction of physically meaningful, low contrast Endmembers that occupy unique image regions.
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Integration of spatial–spectral information for the improved extraction of Endmembers
Remote Sensing of Environment, 2007Co-Authors: Derek Rogge, Benoit Rivard, Jinkai Zhang, A Sanchez, J. R. Harris, Jilu FengAbstract:Spectral-based image endmember extraction methods hinge on the ability to discriminate between pixels based on spectral characteristics alone. Endmembers with distinct spectral features (high spectral contrast) are easy to select, whereas those with minimal unique spectral information (low spectral contrast) are more problematic. Spectral contrast, however, is dependent on the endmember assemblage, such that as the assemblage changes so does the “relative” spectral contrast of each endmember to all other Endmembers. It is then possible for an endmember to have low spectral contrast with respect to the full image, but have high spectral contrast within a subset of the image. The spatial–spectral endmember extraction tool (SSEE) works by analyzing a scene in parts (subsets), such that we increase the spectral contrast of low contrast Endmembers, thus improving the potential for these Endmembers to be selected. The SSEE method comprises three main steps: 1) application of singular value decomposition (SVD) to determine a set of basis vectors that describe most of the spectral variance for subsets of the image; 2) projection of the full image data set onto the locally defined basis vectors to determine a set of candidate endmember pixels; and, 3) imposing spatial constraints for averaging spectrally similar Endmembers, allowing for separation of Endmembers that are spectrally similar, but spatially independent. The SSEE method is applied to two real hyperspectral data sets to demonstrate the effects of imposing spatial constraints on the selection of Endmembers. The results show that the SSEE method is an effective approach to extracting image Endmembers. Specific improvements include the extraction of physically meaningful, low contrast Endmembers that occupy unique image regions.
Paul D Gader - One of the best experts on this subject based on the ideXlab platform.
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SPICEE: An Extension of SPICE for Sparse Endmember Estimation in Hyperspectral Imagery
IEEE Geoscience and Remote Sensing Letters, 2016Co-Authors: Seniha Esen Yuksel, Sefa Kucuk, Paul D GaderAbstract:An extension to the sparsity promoting iterated constrained endmember (SPICE) algorithm, named as SPICEE, has been presented. In ICE and SPICE, Endmembers are estimated using a pseudoinverse method, which may generate Endmembers that are not physically possible when representing normalized reflectance spectra. Although this problem can be alleviated by increasing the regularization, too much regularization leads to finding erroneous Endmembers. To solve these problems, in this letter, a quadratic optimization solution is proposed that constrains the Endmembers to have values between zero and one. The results on three data sets indicate that when regularization is large enough, SPICE and SPICEE generate similar answers; and when regularization is small to none, SPICEE stays more robust. In doing so, besides generating realistic Endmembers, SPICEE helps in decreasing the effort necessary for fine-tuning the regularization parameter.
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piecewise convex multiple model endmember detection and spectral unmixing
IEEE Transactions on Geoscience and Remote Sensing, 2013Co-Authors: Alina Zare, Paul D Gader, Ouiem Bchir, Hichem FriguiAbstract:A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of Endmembers is presented. Hyperspectral data are often nonconvex. The Piecewise Convex Multiple-Model Endmember Detection algorithm accounts for this using a piecewise convex model. Multiple sets of Endmembers and abundances are found using an iterative fuzzy clustering and spectral unmixing method. The results indicate that the piecewise convex representation estimates Endmembers that better represent hyperspectral imagery composed of multiple regions where each region is represented with a distinct set of Endmembers.
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WHISPERS - Bootstrapping for Piece-Wise Convex Endmember Distribution Detection
2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), 2012Co-Authors: Alina Zare, Paul D Gader, Tim Allgire, Dmitri Dranishnikov, Ryan CloseAbstract:A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmember distributions is presented. If Endmembers are represented as random vectors, then they can be characterized by a multivariate probability distribution. These distributions are referred to as endmember distributions. The proposed method combines the Piece-wise Convex Multiple Model Endmember Detection (PCOMMEND) algorithm, the Sparsity Promoting Iterated Constrained Endmembers (SPICE) algorithm, and the Competitive Agglomeration (CA) algorithm to estimate endmember distributions. The goal is to produce distributions that are suitable for inclusion into the Normal Compositional Model (NCM). PCOMMEND forms a fuzzy partition of the spectral pixels into a collection of fuzzy convex sets. Each convex set is defined by a set of Endmembers and the linear mixing model. In this way, non-convex hyperspectral data are more, accurately characterized. The SPICE algorithm estimates the number of Endmembers, the Endmembers, and the abundances for each convex set. This process is repeated several times; each time a set of Endmembers is produced. The collection of all such sets is merged into a single set of Endmembers. This set is clustered using the CA algorithm, which estimates the number of Endmembers by estimating the number of clusters and prototypes for each cluster in the single set of Endmembers. These prototypes are taken to be the means of endmember distributions. The covariances are estimated by assigning each endmember to the closest prototype and estimating the covariance of that set. The resulting distributions are suitable for the NCM model. Results are shown for the PAVIA data set.
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L1-Endmembers: a robust endmember detection and spectral unmixing algorithm
Algorithms and Technologies for Multispectral Hyperspectral and Ultraspectral Imagery XVI, 2010Co-Authors: Alina Zare, Paul D GaderAbstract:A hyperspectral endmember detection and spectral unmixing algorithm based on an l 1 norm factorization of the input hyperspectral data is developed and compared to a method based on l 2 norm factorization. Both algorithms, the L1-Endmembers algorithm based on the l 1 norm and the SPICE algorithm based on the l 2 norm, simultaneously and autonomously estimate endmember spectra, abundance values and the number of Endmembers needed for a hyperspectral image. The l 1 norm factorization of the hyperspectral data is approximated through the use of the Huber M-estimator. Results showing the stability of the L1-Endmembers algorithm in terms of the number of Endmembers estimated with noise and outliers are presented. Results indicate that the proposed algorithm is more consistent in estimating the correct number of Endmembers over SPICE. However, when both algorithms determine the correct number of Endmembers, SPICE results provide a better estimate of Endmembers and a lower variance of endmember estimates over many runs with random initialization.
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PCE: Piecewise Convex Endmember Detection
IEEE Transactions on Geoscience and Remote Sensing, 2010Co-Authors: Alina Zare, Paul D GaderAbstract:A new hyperspectral endmember detection method that represents Endmembers as distributions, autonomously partitions the input data set into several convex regions, and simultaneously determines endmember distributions (EDs) and proportion values for each convex region is presented. Spectral unmixing methods that treat Endmembers as distributions or hyperspectral images as piecewise convex data sets have not been previously developed. Piecewise convex endmember (PCE) detection can be viewed in two parts. The first part, the ED detection algorithm, estimates a distribution for each endmember rather than estimating a single spectrum. By using EDs, PCE can incorporate an endmember's inherent spectral variation and the variation due to changing environmental conditions. ED uses a new sparsity-promoting polynomial prior while estimating abundance values. The second part of PCE partitions the input hyperspectral data set into convex regions and estimates EDs and proportions for each of these regions. The number of convex regions is determined autonomously using the Dirichlet process. PCE is effective at handling highly mixed hyperspectral images where all of the pixels in the scene contain mixtures of multiple Endmembers. Furthermore, each convex region found by PCE conforms to the convex geometry model for hyperspectral imagery. This model requires that the proportions associated with a pixel be nonnegative and sum to one. Algorithm results on hyperspectral data indicate that PCE produces Endmembers that represent the true ground-truth classes of the input data set. The algorithm can also effectively represent Endmembers as distributions, thus incorporating an endmember's spectral variability.
Liangpei Zhang - One of the best experts on this subject based on the ideXlab platform.
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Saliency-Based Endmember Detection for Hyperspectral Imagery
IEEE Transactions on Geoscience and Remote Sensing, 2018Co-Authors: Xinyu Wang, Yanfei Zhong, Liangpei ZhangAbstract:This paper focuses on the endmember extraction (EE) technique for analyzing hyperspectral images. We first prove that the reconstruction errors (REs) and abundance anomalies (AAs) (abundances that fail to satisfy the abundance constraints) are effective in extracting undetected Endmembers. Then, according to the spatial continuity of the endmember objects and differing from noise or outliers with a sparse distribution, the Endmembers are assumed to be located at some salient areas in the RE and AA maps. A novel EE algorithm termed saliency-based endmember detection (SED) is proposed, where the visual saliency model is introduced to explore and analyze the spatial information that is contained in the AA and RE maps. Specifically, the AA and RE maps are regarded as the visual inputs, whereas the Endmembers are treated as the visual stimuli. In SED, we assume that the pure pixel assumption holds. Based on the characteristics of the human visual system, the proposed method can not only extract Endmembers in homogenous areas, but it can also highlight the small targets whose abundances may be spatially varied. In addition, since the spatial information is exploited in the reconstruction, the capability of the Endmembers to represent the hyperspectral scene is automatically considered in the process of EE, and the detected Endmembers are both accurate and reliable. The experimental results obtained on both simulated and real hyperspectral data confirm the merits and viability of the proposed algorithm.
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Endmember number estimation for hyperspectral imagery based on vertex component analysis
Journal of Applied Remote Sensing, 2014Co-Authors: Rong Liu, Liangpei ZhangAbstract:Endmember extraction is a crucial step in hyperspectral unmixing. For many endmember extraction algorithms, the number of Endmembers is a precondition, and the accuracy of the endmember number directly affects the quality of the unmixing results. This paper proposes an automatic method for estimating the endmember number for hyperspectral imagery on the basis of vertex component analysis (VCA). The endmember extraction result of VCA is inconsistent because of the involvement of random vectors. This feature is utilized by our method to obtain the real endmember number. First, the endmember number is initialized with a small integer. Then, VCA is repeatedly implemented, and the endmember extraction results are different each time because of VCA’s inconsistency. Finally, the real endmember number is determined from the union of these individual results. Extensive experiments were carried out on both simulated and real hyperspectral images, confirming the effectiveness of the proposed approach.
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Spatial-Spectral Information Based Abundance-Constrained Endmember Extraction Methods
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014Co-Authors: Liangpei ZhangAbstract:Endmember extraction, which is an important technique for hyperspectral data interpretation, selects a collection of pure signature spectra of the different materials, called Endmembers, which are present in a remotely sensed hyperspectral image scene. These pure signatures are then used in spectral unmixing algorithms to decompose the scene into abundance fractions, which indicate the proportion of each endmember's presence in a mixed pixel. In other words, abundances can be obtained by the given Endmembers. Correspondingly, Endmembers can be extracted based on an abundance constraint. In this paper, we first propose an endmember extraction framework based on an abundance constraint whose efficiency is related to the abundance calculation. The mainstream existing spatial-spectral algorithms can have a very high complexity and are sensitive to outliers, or the spatial information is considered followed by the spectral information. We therefore propose a strategy to consider the spectral information followed by the spatial information, using an abundance-constrained framework. The spatial strategy is also assumed to be immune to outliers. Experiments on both synthetic and real hyperspectral data sets indicate that: 1) the abundance constraint is effective for endmember extraction; and 2) the proposed spatial processing method used in the abundance-constrained endmember extraction framework can effectively avoid outliers.
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A Hybrid Automatic Endmember Extraction Algorithm Based on a Local Window
IEEE Transactions on Geoscience and Remote Sensing, 2011Co-Authors: Liangpei ZhangAbstract:Anomaly Endmembers play an important role in the application of remote sensing, such as in unmixing classification and target detection. Inspired by the iterative error analysis (IEA), a hybrid endmember extraction algorithm (HEEA) based on a local window is proposed in this paper, which focuses on improving the accuracy of endmember extraction. HEEA uses the spectral-information-divergence-spectral-angle-distance metric to measure the similarity and the orthogonal subspace projection (OSP) method to search for the Endmembers, which can decrease the correlation between extracted endmember spectra. Moreover, it is based on a local window which integrates both spatial and spectral aspects to extract Endmembers. A synthetic image and Airborne Visible/Infrared Imaging Spectrometer data were tested with the HEEA method, classical IEA, OSP, simplex growing algorithm, sequential maximum angle convex cone, and spectral spatial endmember extraction automatic endmember extraction method. Experimental results indicated that HEEA manifested a slightly better improvement in the rmse and spectrum information than the other methods. The effect was investigated with various SNRs and different window sizes. The robustness of HEEA is better than the classical IEA, even with lower SNR.
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improving the quality of extracted Endmembers
Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009Co-Authors: Liangpei Zhang, Nareenart RaksuntornAbstract:Endmember extraction for spectral mixture analysis is a necessary step when endmember information is unknown. If Endmembers are assumed to be pure pixels present in an image scene, endmember extraction is to search the most distinctive pixels. Popular algorithms using the criteria of simplex volume maximization (e.g., N-FINDR) and spectral signature similarity (e.g., Vertex Component Analysis) belong to this type. If pure pixel assumption is not imposed, endmember extraction usually is conducted by searching the signatures that can circumscribe the data cloud with the minimum volume. Both types of algorithms are affected by anomalous pixels since such outliers are very different from other pixels and act as interferers during simplex volume evaluation. In this paper, we propose a new approach that separates the endmember searching in normal and anomalous pixels. Real data experiments show that it can improve the quality of extracted Endmembers.
Alina Zare - One of the best experts on this subject based on the ideXlab platform.
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Earth Movers Distance-Based Simultaneous Comparison of Hyperspectral Endmembers and Proportions
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014Co-Authors: Alina Zare, Derek T. AndersonAbstract:A new approach for simultaneously comparing sets of hyperspectral Endmembers and proportion values using the Earth Movers Distance (EMD) is presented. First, the EMD is defined and calculated per-pixel based on the proportion values and corresponding Endmembers. Next, these per-pixel EMD distances are aggregated to obtain a final measure of dissimilarity. In particular, the proposed EMD approach can be used to simultaneously compare Endmembers and proportion values with differing numbers of Endmembers. The proposed method has a number of uses, including: computing the similarity between two sets of Endmembers and proportion values that were obtained using any algorithm or underlying mixing model, clustering sets of hyperspectral endmember and proportion values, or evaluating spectral unmixing results by comparing estimated values to ground truth information. Experiments on both simulated and measured hyperspectral data sets demonstrate that the EMD is effective at simultaneous endmember and proportion comparison.
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piecewise convex multiple model endmember detection and spectral unmixing
IEEE Transactions on Geoscience and Remote Sensing, 2013Co-Authors: Alina Zare, Paul D Gader, Ouiem Bchir, Hichem FriguiAbstract:A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of Endmembers is presented. Hyperspectral data are often nonconvex. The Piecewise Convex Multiple-Model Endmember Detection algorithm accounts for this using a piecewise convex model. Multiple sets of Endmembers and abundances are found using an iterative fuzzy clustering and spectral unmixing method. The results indicate that the piecewise convex representation estimates Endmembers that better represent hyperspectral imagery composed of multiple regions where each region is represented with a distinct set of Endmembers.
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WHISPERS - Bootstrapping for Piece-Wise Convex Endmember Distribution Detection
2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), 2012Co-Authors: Alina Zare, Paul D Gader, Tim Allgire, Dmitri Dranishnikov, Ryan CloseAbstract:A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmember distributions is presented. If Endmembers are represented as random vectors, then they can be characterized by a multivariate probability distribution. These distributions are referred to as endmember distributions. The proposed method combines the Piece-wise Convex Multiple Model Endmember Detection (PCOMMEND) algorithm, the Sparsity Promoting Iterated Constrained Endmembers (SPICE) algorithm, and the Competitive Agglomeration (CA) algorithm to estimate endmember distributions. The goal is to produce distributions that are suitable for inclusion into the Normal Compositional Model (NCM). PCOMMEND forms a fuzzy partition of the spectral pixels into a collection of fuzzy convex sets. Each convex set is defined by a set of Endmembers and the linear mixing model. In this way, non-convex hyperspectral data are more, accurately characterized. The SPICE algorithm estimates the number of Endmembers, the Endmembers, and the abundances for each convex set. This process is repeated several times; each time a set of Endmembers is produced. The collection of all such sets is merged into a single set of Endmembers. This set is clustered using the CA algorithm, which estimates the number of Endmembers by estimating the number of clusters and prototypes for each cluster in the single set of Endmembers. These prototypes are taken to be the means of endmember distributions. The covariances are estimated by assigning each endmember to the closest prototype and estimating the covariance of that set. The resulting distributions are suitable for the NCM model. Results are shown for the PAVIA data set.
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L1-Endmembers: a robust endmember detection and spectral unmixing algorithm
Algorithms and Technologies for Multispectral Hyperspectral and Ultraspectral Imagery XVI, 2010Co-Authors: Alina Zare, Paul D GaderAbstract:A hyperspectral endmember detection and spectral unmixing algorithm based on an l 1 norm factorization of the input hyperspectral data is developed and compared to a method based on l 2 norm factorization. Both algorithms, the L1-Endmembers algorithm based on the l 1 norm and the SPICE algorithm based on the l 2 norm, simultaneously and autonomously estimate endmember spectra, abundance values and the number of Endmembers needed for a hyperspectral image. The l 1 norm factorization of the hyperspectral data is approximated through the use of the Huber M-estimator. Results showing the stability of the L1-Endmembers algorithm in terms of the number of Endmembers estimated with noise and outliers are presented. Results indicate that the proposed algorithm is more consistent in estimating the correct number of Endmembers over SPICE. However, when both algorithms determine the correct number of Endmembers, SPICE results provide a better estimate of Endmembers and a lower variance of endmember estimates over many runs with random initialization.
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PCE: Piecewise Convex Endmember Detection
IEEE Transactions on Geoscience and Remote Sensing, 2010Co-Authors: Alina Zare, Paul D GaderAbstract:A new hyperspectral endmember detection method that represents Endmembers as distributions, autonomously partitions the input data set into several convex regions, and simultaneously determines endmember distributions (EDs) and proportion values for each convex region is presented. Spectral unmixing methods that treat Endmembers as distributions or hyperspectral images as piecewise convex data sets have not been previously developed. Piecewise convex endmember (PCE) detection can be viewed in two parts. The first part, the ED detection algorithm, estimates a distribution for each endmember rather than estimating a single spectrum. By using EDs, PCE can incorporate an endmember's inherent spectral variation and the variation due to changing environmental conditions. ED uses a new sparsity-promoting polynomial prior while estimating abundance values. The second part of PCE partitions the input hyperspectral data set into convex regions and estimates EDs and proportions for each of these regions. The number of convex regions is determined autonomously using the Dirichlet process. PCE is effective at handling highly mixed hyperspectral images where all of the pixels in the scene contain mixtures of multiple Endmembers. Furthermore, each convex region found by PCE conforms to the convex geometry model for hyperspectral imagery. This model requires that the proportions associated with a pixel be nonnegative and sum to one. Algorithm results on hyperspectral data indicate that PCE produces Endmembers that represent the true ground-truth classes of the input data set. The algorithm can also effectively represent Endmembers as distributions, thus incorporating an endmember's spectral variability.