The Experts below are selected from a list of 7008 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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A New Algorithm for Bilinear Spectral Unmixing of HyperSpectral Images Using Particle Swarm Optimization
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Wenfei Luo, Antonio Plaza, Paolo Gamba, Liang Zhong, Lianru Gao, Andrea Marinoni, Bin Yang, Bing ZhangAbstract:Spectral Unmixing is an important technique for exploiting hyperSpectral data. The presence of nonlinear mixing effects poses an important problem when attempting to provide accurate estimates of the abundance fractions of pure Spectral components (endmembers) in a scene. This problem complicates the development of algorithms that can address all types of nonlinear mixtures in the scene. In this paper, we develop a new strategy to simultaneously estimate both the endmember signatures and their corresponding abundances using a biswarm particle swarm optimization (BiPSO) bilinear Unmixing technique based on Fan's model. Our main motivation in this paper is to explore the potential of the newly proposed bilinear mixture model based on particle swarm optimization (PSO) for nonlinear Spectral Unmixing purposes. By taking advantage of the learning mechanism provided by PSO, we embed a multiobjective optimization technique into the algorithm to handle the more complex constraints in simplex volume minimization algorithms for Spectral Unmixing, thus avoiding limitations due to penalty factors. Our experimental results, conducted using both synthetic and real hyperSpectral data, demonstrate that the proposed BiPSO algorithm can outperform other traditional Spectral Unmixing techniques by accounting for nonlinearities in the mixtures present in the scene.
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HyperMix: An Open-Source Tool for Fast Spectral Unmixing on Graphics Processing Units
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Luis Ignacio Jimenez, Antonio PlazaAbstract:Spectral Unmixing has been a popular technique for analyzing remotely sensed hyperSpectral images. The goal of Unmixing is to find a collection of pure Spectral constituents (called endmembers ) that can explain each (possibly mixed) pixel of the scene as a combination of endmembers, weighted by their coverage fractions in the pixel or abundances . Over the last years, many algorithms have been presented to address the three main parts of the Spectral Unmixing chain: 1) estimation of the number of endmembers; 2) identification of the endmember signatures; and 3) estimation of the per-pixel fractional abundances. However, to date, there is no standardized tool that integrates these algorithms in a unified framework. In this letter, we present HyperMix, an open-source tool for Spectral Unmixing that integrates different approaches for Spectral Unmixing and allows building Unmixing chains in graphical fashion, so that the end-user can define one or several Spectral Unmixing chains in fully configurable mode. HyperMix provides efficient implementations of most of the algorithms used for Spectral Unmixing, so that the tool automatically recognizes if the computer has a graphics processing unit (GPU) available and optimizes the execution of these algorithms in the GPU. This allows for the execution of Spectral Unmixing chains on large hyperSpectral scenes in computationally efficient fashion. The tool is available online from http://hypercomphypermix.blogspot.com.es and has been validated with real hyperSpectral scenes, providing state-of-the-art Unmixing results.
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Complementarity of Discriminative Classifiers and Spectral Unmixing Techniques for the Interpretation of HyperSpectral Images
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Inmaculada Dopido, Paolo Gamba, Antonio PlazaAbstract:Classification and Spectral Unmixing are two important techniques for hyperSpectral data exploitation. Traditionally, these techniques have been exploited independently. In this paper, we propose a new technique that exploits their complementarity. Specifically, we develop a new framework for semisupervised hyperSpectral image classification that naturally integrates the information provided by discriminative classification and Spectral Unmixing. The idea is to assign more confidence to the information provided by discriminative classification for those pixels that can be easily catalogued due to their Spectral purity. For those pixels that are more highly mixed in nature, we assign more confidence to the information provided by Spectral Unmixing. In this case, we use a traditional Spectral Unmixing chain to produce the abundance fractions of the pure signatures (endmembers) that model the mixture information at a subpixel level. The decision on which source of information is prioritized in the process is taken adaptively, when new unlabeled samples are selected and included in our semisupervised framework. In this regard, the proposed approach can adaptively integrate these two sources of information without the need to establish any weight parameters, thus exploiting the complementarity of classification and Unmixing and selecting the most appropriate source of information in each case. In order to test our concept, which has similar computational complexity as traditional semisupervised classification strategies, we have used two different hyperSpectral data sets with different characteristics and spatial resolution. In our experiments, we consider two different discriminative classifiers: multinomial logistic regression and probabilistic support vector machine. The obtained results indicate that the proposed approach, which jointly exploits the features provided by classification and Spectral Unmixing in adaptive fashion, offers an effective solution to improve classification performance in hyperSpectral scenes containing mixed pixels.
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Binary partition tree-based local Spectral Unmixing
2014Co-Authors: Lucas Drumetz, Antonio Plaza, Miguel Angel Veganzones, Ruben Marrero, Guillaume Tochon, Mauro Dalla Mura, Jocelyn ChanussotAbstract:The linear mixing model (LMM) is a widely used methodology for the Spectral Unmixing (SU) of hyperSpectral data. In this model, hyperSpectral data is formed as a linear combination of Spectral signatures corresponding to macroscopically pure materials (endmembers), weighted by their fractional abundances. Some of the drawbacks of the LMM are the presence of multiple mixtures and the Spectral variability of the endmembers due to illumination and atmospheric effects. These issues appear as variations of the Spectral conditions of the image along its spatial domain. However, these effects are not so severe locally and could be at least mitigated by working in smaller regions of the image. The proposed local SU works over a partition of the image, performing the Spectral Unmixing locally in each region of the partition. In this work, we first introduce the general local SU methodology, then we propose an implementation of the local SU based on a binary partition tree representation of the hyperSpectral image and finally we give an experimental validation of the approach using real data.
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Semi-supervised classification of urban hyperSpectral data using Spectral Unmixing concepts
Joint Urban Remote Sensing Event 2013, 2013Co-Authors: Inmaculada Dopido, Antonio Plaza, Paolo GambaAbstract:Spectral Unmixing and classification have been widely used in the recent literature to analyze remotely sensed hyperSpectral data. However, possible connections between semi-supervised classification and Spectral Unmixing concepts have been rarely investigated. In this work, we propose a new method to perform semi-supervised classification of urban hyperSpectral images by exploiting the information retrieved with Spectral Unmixing. The proposed approach integrates a well-established discriminative classifier (multinomial logistic regression) with two different Spectral Unmixing chains, thus bridging the gap between Unmixing and classification. Moreover, the proposed method uses active learning when generating new unlabeled samples for classification.
Liguo Wang - One of the best experts on this subject based on the ideXlab platform.
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Reduction of Spectral Unmixing Uncertainty Using Minimum-Class-Variance Support Vector Machines
IEEE Geoscience and Remote Sensing Letters, 2016Co-Authors: Xiaofeng Li, Liguo Wang, Kai ZhaoAbstract:Several Spectral Unmixing techniques using multiple endmembers for each class have been developed. Although they can address within-class Spectral variability, their Unmixing results may have low Unmixing resolution when the within-class variation is large due to the associated high uncertainty. Therefore, it is critical to represent data in an effective feature space so that the endmember classes are compact with small variation. In this letter, a minimum-class-variance support vector machine (MCVSVM) is further developed to extend its functions for both classification and Spectral Unmixing. Moreover, analytical expressions for Spectral Unmixing resolution (SUR) are provided to measure the Spectral Unmixing uncertainty in the new feature space. The extended MCVSVM (e_MCVSVM) can improve SUR and reduce the Spectral Unmixing uncertainty as it can effectively maximize the between-class scatter while minimizing the within-class scatter. Experimental results show that the e_MCVSVM algorithm performs better in terms of the Unmixing accuracy and the computation speed compared with the other algorithms (e.g., fully constrained least squares and endmember bundles) in both linearly separable and nonseparable cases. This newly proposed approach advances the linear Spectral mixture analysis with greater speed and higher accuracy based on the SVM after the SUR is effectively characterized.
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Spectral Unmixing with estimated adaptive endmember index using extended support vector machine
Spatial diversity and dynamics in resources and urban development: Volume 1: Regional resources, 2015Co-Authors: Chandrama Sarker, Liguo Wang, Xiuping Jia, Donald Fraser, Leo LymburnerAbstract:The most difficult operation in flood inundation mapping using optical flood images is to separate fully inundated areas from the “wet” areas where trees and houses are partly covered by water: this can be referred as a typical problem, the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because of the presence of mixed pixels in the images. To solve the mixed pixel problem, advanced image processing techniques are adopted, and the linear Spectral Unmixing method is one of the most popular soft classification techniques used for mixed pixel analysis. The good performance of linear Spectral Unmixing depends on two important issues: the method of selecting endmembers and the method to model the endmembers for Unmixing. This chapter presents an improvement in the adaptive selection of the endmember subset for each pixel in the Spectral Unmixing method for reliable flood mapping. Using a fixed set of endmembers for Spectral Unmixing all pixels in an entire image might cause overestimation of the endmember spectra residing in a mixed pixel and hence reduce the performance level of Spectral Unmixing. Compared to this, application of an estimated adaptive subset of endmembers for each pixel can decrease the residual error in Unmixing results and provide a reliable output. In this chapter, it has also been proved that this proposed method can improve the accuracy of conventional linear Unmixing methods and is also easy to apply. Three different linear Spectral Unmixing methods were applied to test the improvement in Unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.
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Spectral Unmixing Technique of HSI
Hyperspectral Image Processing, 2015Co-Authors: Liguo Wang, Chunhui ZhaoAbstract:Relative to the classification technique, the Spectral Unmixing (Keshava and Mustard in IEEE Trans Sig Process Mag 19:44–57, 2002) i.e., soft classification technique started late. Although the Spectral resolution of the hyperSpectral image has been improved greatly, the spatial resolution of the corresponding land object target of the pixel has been relatively low.
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On Spectral Unmixing Resolution Using Extended Support Vector Machines
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Xiaofeng Li, Liguo Wang, Kai ZhaoAbstract:Due to the limited spatial resolution of multiSpectral/hyperSpectral data, mixed pixels widely exist and various Spectral Unmixing techniques have been developed for information extraction at the subpixel level in recent years. One of the challenging problems in Spectral mixture analysis is how to model the data of a primary class. Given that the within-class Spectral variability (WSV) is inevitable, it is more realistic to associate a group of representative spectra with a pure class. The Unmixing method using the extended support vector machines (eSVMs) has handled this problem effectively. However, it has simplified WSV in the mixed cases. In this paper, a further development of eSVMs is presented to address two problems in multiple-endmember Spectral mixture analysis: 1) one mixed pixel may be unmixed into different fractions (model overlap); and 2) one fraction may correspond to a group of mixed pixels (fraction overlap). Then, Spectral Unmixing resolution (SUR) is introduced to characterize how finely the mixture in a mixed pixel can be quantified. The quantitative relationship between SUR and WSV of endmembers is derived via a geometry analysis in support vector machine feature space. Thus, the possible SUR can be estimated when multiple endmembers for each class are given. Moreover, if the requirement of SUR is fixed, the acceptance level of WSV is then limited, which can be used as a guide to remove outliers and purify endmembers for each primary class. Experiments are presented to illustrate model and fraction overlap problems and the application of SUR in uncertainty analysis of Spectral Unmixing.
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Fuzzy Assessment of Spectral Unmixing Algorithms
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014Co-Authors: Xiuping Jia, Liguo WangAbstract:While a single spectrum is often used to present a pure class, it is more realistic to consider intra-class Spectral variation and model a pure class using a group of its representative spectra. In line with this consideration, crisp Unmixing accuracy assessment, where Unmixing performance is assessed using a mean squared error of the estimated endmember fractions, can be misleading. In this paper, alterative Spectral Unmixing assessment methods are introduced to account for the uncertainty contained in the Spectral measurements and during the ground truth data collection. Two fuzzy measures are developed to assess Unmixing performance. One is fuzzy Unmixing fraction error for a realistic assessment and the other is pixel level Unmixing accuracy to provide a good quantitative understanding of the Unmixing success rates spatially. To demonstrate and illustrate how they work, the two fuzzy measures are applied to evaluate the performance of several Spectral Unmixing methods including both single spectrum based and multiple spectra based algorithms. Crisp assessments and fuzzy results at various tolerance levels are presented and compared. Based on the realistic measures proposed, it is found the recent developed Unmixing method with extended Support Vector Machines outperforms other algorithms tested.
Paolo Gamba - One of the best experts on this subject based on the ideXlab platform.
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A New Algorithm for Bilinear Spectral Unmixing of HyperSpectral Images Using Particle Swarm Optimization
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Wenfei Luo, Antonio Plaza, Paolo Gamba, Liang Zhong, Lianru Gao, Andrea Marinoni, Bin Yang, Bing ZhangAbstract:Spectral Unmixing is an important technique for exploiting hyperSpectral data. The presence of nonlinear mixing effects poses an important problem when attempting to provide accurate estimates of the abundance fractions of pure Spectral components (endmembers) in a scene. This problem complicates the development of algorithms that can address all types of nonlinear mixtures in the scene. In this paper, we develop a new strategy to simultaneously estimate both the endmember signatures and their corresponding abundances using a biswarm particle swarm optimization (BiPSO) bilinear Unmixing technique based on Fan's model. Our main motivation in this paper is to explore the potential of the newly proposed bilinear mixture model based on particle swarm optimization (PSO) for nonlinear Spectral Unmixing purposes. By taking advantage of the learning mechanism provided by PSO, we embed a multiobjective optimization technique into the algorithm to handle the more complex constraints in simplex volume minimization algorithms for Spectral Unmixing, thus avoiding limitations due to penalty factors. Our experimental results, conducted using both synthetic and real hyperSpectral data, demonstrate that the proposed BiPSO algorithm can outperform other traditional Spectral Unmixing techniques by accounting for nonlinearities in the mixtures present in the scene.
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Complementarity of Discriminative Classifiers and Spectral Unmixing Techniques for the Interpretation of HyperSpectral Images
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Inmaculada Dopido, Paolo Gamba, Antonio PlazaAbstract:Classification and Spectral Unmixing are two important techniques for hyperSpectral data exploitation. Traditionally, these techniques have been exploited independently. In this paper, we propose a new technique that exploits their complementarity. Specifically, we develop a new framework for semisupervised hyperSpectral image classification that naturally integrates the information provided by discriminative classification and Spectral Unmixing. The idea is to assign more confidence to the information provided by discriminative classification for those pixels that can be easily catalogued due to their Spectral purity. For those pixels that are more highly mixed in nature, we assign more confidence to the information provided by Spectral Unmixing. In this case, we use a traditional Spectral Unmixing chain to produce the abundance fractions of the pure signatures (endmembers) that model the mixture information at a subpixel level. The decision on which source of information is prioritized in the process is taken adaptively, when new unlabeled samples are selected and included in our semisupervised framework. In this regard, the proposed approach can adaptively integrate these two sources of information without the need to establish any weight parameters, thus exploiting the complementarity of classification and Unmixing and selecting the most appropriate source of information in each case. In order to test our concept, which has similar computational complexity as traditional semisupervised classification strategies, we have used two different hyperSpectral data sets with different characteristics and spatial resolution. In our experiments, we consider two different discriminative classifiers: multinomial logistic regression and probabilistic support vector machine. The obtained results indicate that the proposed approach, which jointly exploits the features provided by classification and Spectral Unmixing in adaptive fashion, offers an effective solution to improve classification performance in hyperSpectral scenes containing mixed pixels.
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Semi-supervised classification of urban hyperSpectral data using Spectral Unmixing concepts
Joint Urban Remote Sensing Event 2013, 2013Co-Authors: Inmaculada Dopido, Antonio Plaza, Paolo GambaAbstract:Spectral Unmixing and classification have been widely used in the recent literature to analyze remotely sensed hyperSpectral data. However, possible connections between semi-supervised classification and Spectral Unmixing concepts have been rarely investigated. In this work, we propose a new method to perform semi-supervised classification of urban hyperSpectral images by exploiting the information retrieved with Spectral Unmixing. The proposed approach integrates a well-established discriminative classifier (multinomial logistic regression) with two different Spectral Unmixing chains, thus bridging the gap between Unmixing and classification. Moreover, the proposed method uses active learning when generating new unlabeled samples for classification.
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TyWRRS - Semi-supervised classification of hyperSpectral data using Spectral Unmixing concepts
2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS), 2012Co-Authors: Inmaculada Dopido, Antonio Plaza, Paolo GambaAbstract:Spectral Unmixing and classification have been widely used in the recent literature to analyze remotely sensed hyperSpectral data. However, possible connections between semi-supervised classification and Spectral Unmixing concepts have been rarely investigated. In this work, we propose a new method to perform semi-supervised classification of hyperSpectral images by exploiting the information retrieved with Spectral Unmixing. The proposed method integrates a well-established discriminative classifier (multinomial logistic regression) with different Spectral Unmixing chains, thus bridging the gap between Unmixing and classification. Furthermore, the proposed method uses active learning when generating new unlabeled samples for classification. The proposed method is experimentally validated using real hyperSpectral data sets, indicating that the combination of Spectral Unmixing and semi-supervised classification can lead to powerful new algorithms for hyperSpectral data interpretation.
Xiuping Jia - One of the best experts on this subject based on the ideXlab platform.
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Spectral Unmixing with estimated adaptive endmember index using extended support vector machine
Spatial diversity and dynamics in resources and urban development: Volume 1: Regional resources, 2015Co-Authors: Chandrama Sarker, Liguo Wang, Xiuping Jia, Donald Fraser, Leo LymburnerAbstract:The most difficult operation in flood inundation mapping using optical flood images is to separate fully inundated areas from the “wet” areas where trees and houses are partly covered by water: this can be referred as a typical problem, the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because of the presence of mixed pixels in the images. To solve the mixed pixel problem, advanced image processing techniques are adopted, and the linear Spectral Unmixing method is one of the most popular soft classification techniques used for mixed pixel analysis. The good performance of linear Spectral Unmixing depends on two important issues: the method of selecting endmembers and the method to model the endmembers for Unmixing. This chapter presents an improvement in the adaptive selection of the endmember subset for each pixel in the Spectral Unmixing method for reliable flood mapping. Using a fixed set of endmembers for Spectral Unmixing all pixels in an entire image might cause overestimation of the endmember spectra residing in a mixed pixel and hence reduce the performance level of Spectral Unmixing. Compared to this, application of an estimated adaptive subset of endmembers for each pixel can decrease the residual error in Unmixing results and provide a reliable output. In this chapter, it has also been proved that this proposed method can improve the accuracy of conventional linear Unmixing methods and is also easy to apply. Three different linear Spectral Unmixing methods were applied to test the improvement in Unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.
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Fuzzy Assessment of Spectral Unmixing Algorithms
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014Co-Authors: Xiuping Jia, Liguo WangAbstract:While a single spectrum is often used to present a pure class, it is more realistic to consider intra-class Spectral variation and model a pure class using a group of its representative spectra. In line with this consideration, crisp Unmixing accuracy assessment, where Unmixing performance is assessed using a mean squared error of the estimated endmember fractions, can be misleading. In this paper, alterative Spectral Unmixing assessment methods are introduced to account for the uncertainty contained in the Spectral measurements and during the ground truth data collection. Two fuzzy measures are developed to assess Unmixing performance. One is fuzzy Unmixing fraction error for a realistic assessment and the other is pixel level Unmixing accuracy to provide a good quantitative understanding of the Unmixing success rates spatially. To demonstrate and illustrate how they work, the two fuzzy measures are applied to evaluate the performance of several Spectral Unmixing methods including both single spectrum based and multiple spectra based algorithms. Crisp assessments and fuzzy results at various tolerance levels are presented and compared. Based on the realistic measures proposed, it is found the recent developed Unmixing method with extended Support Vector Machines outperforms other algorithms tested.
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Spectral Unmixing in Multiple-Kernel Hilbert Space for HyperSpectral Imagery
IEEE Transactions on Geoscience and Remote Sensing, 2013Co-Authors: Shizhe Wang, Xiuping JiaAbstract:In this paper, we address a Spectral Unmixing problem for hyperSpectral images by introducing multiple-kernel learning (MKL) coupled with support vector machines. To effectively solve issues of Spectral Unmixing, an MKL method is explored to build new boundaries and distances between classes in multiple-kernel Hilbert space (MKHS). Integrating reproducing kernel Hilbert spaces (RKHSs) spanned by a series of different basis kernels in MKHS is able to provide increased power in handling general nonlinear problems than traditional single-kernel learning in RKHS. The proposed method is developed to solve multiclass Unmixing problems. To validate the proposed MKL-based algorithm, both synthetic data and real hyperSpectral image data were used in our experiments. The experimental results demonstrate that the proposed algorithm has a strong ability to capture interclass Spectral differences and improve Unmixing accuracy, compared to the state-of-the-art algorithms tested.
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Foreword to the Special Issue on Spectral Unmixing of Remotely Sensed Data
IEEE Transactions on Geoscience and Remote Sensing, 2011Co-Authors: Antonio Plaza, Xiuping Jia, Jose M. Bioucas-dias, Fred A. KruseAbstract:The 19 papers in this special issue focus on the state-of-the-art and most recent developments in the area of Spectral Unmixing of remotely sensed data.
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controlled Spectral Unmixing using extended support vector machines
Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010Co-Authors: Xiuping Jia, Chandrama Dey, Donald Fraser, Leo Lymburner, Adam LewisAbstract:This paper presents an improved Spectral Unmixing framework for remote sensing data interpretation. Instead of Unmixing every pixel in an image into a fixed set of endmembers, approaches of adaptive subsets of endmember selection for individual pixels are presented which can improve the performance of Spectral Unmixing. An integrated hard and soft classification map is then generated by applying the mixture analysis based on extended Support Vector Machines. The proposed treatment is effective and easy to implement. Unmixing is more reliable with the controlled mixture model. It can cope with the endmembers' Spectral variation as a result of system noise encountered during data collection from the space. Experiments were conducted with Landsat ETM data and satisfactory results were achieved.
Kai Zhao - One of the best experts on this subject based on the ideXlab platform.
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Reduction of Spectral Unmixing Uncertainty Using Minimum-Class-Variance Support Vector Machines
IEEE Geoscience and Remote Sensing Letters, 2016Co-Authors: Xiaofeng Li, Liguo Wang, Kai ZhaoAbstract:Several Spectral Unmixing techniques using multiple endmembers for each class have been developed. Although they can address within-class Spectral variability, their Unmixing results may have low Unmixing resolution when the within-class variation is large due to the associated high uncertainty. Therefore, it is critical to represent data in an effective feature space so that the endmember classes are compact with small variation. In this letter, a minimum-class-variance support vector machine (MCVSVM) is further developed to extend its functions for both classification and Spectral Unmixing. Moreover, analytical expressions for Spectral Unmixing resolution (SUR) are provided to measure the Spectral Unmixing uncertainty in the new feature space. The extended MCVSVM (e_MCVSVM) can improve SUR and reduce the Spectral Unmixing uncertainty as it can effectively maximize the between-class scatter while minimizing the within-class scatter. Experimental results show that the e_MCVSVM algorithm performs better in terms of the Unmixing accuracy and the computation speed compared with the other algorithms (e.g., fully constrained least squares and endmember bundles) in both linearly separable and nonseparable cases. This newly proposed approach advances the linear Spectral mixture analysis with greater speed and higher accuracy based on the SVM after the SUR is effectively characterized.
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On Spectral Unmixing Resolution Using Extended Support Vector Machines
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Xiaofeng Li, Liguo Wang, Kai ZhaoAbstract:Due to the limited spatial resolution of multiSpectral/hyperSpectral data, mixed pixels widely exist and various Spectral Unmixing techniques have been developed for information extraction at the subpixel level in recent years. One of the challenging problems in Spectral mixture analysis is how to model the data of a primary class. Given that the within-class Spectral variability (WSV) is inevitable, it is more realistic to associate a group of representative spectra with a pure class. The Unmixing method using the extended support vector machines (eSVMs) has handled this problem effectively. However, it has simplified WSV in the mixed cases. In this paper, a further development of eSVMs is presented to address two problems in multiple-endmember Spectral mixture analysis: 1) one mixed pixel may be unmixed into different fractions (model overlap); and 2) one fraction may correspond to a group of mixed pixels (fraction overlap). Then, Spectral Unmixing resolution (SUR) is introduced to characterize how finely the mixture in a mixed pixel can be quantified. The quantitative relationship between SUR and WSV of endmembers is derived via a geometry analysis in support vector machine feature space. Thus, the possible SUR can be estimated when multiple endmembers for each class are given. Moreover, if the requirement of SUR is fixed, the acceptance level of WSV is then limited, which can be used as a guide to remove outliers and purify endmembers for each primary class. Experiments are presented to illustrate model and fraction overlap problems and the application of SUR in uncertainty analysis of Spectral Unmixing.
