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Hyperspectral Remote Sensing Data Analysis and Future Challenges
"... Abstract—Hyperspectral remote sensing ..."
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A signal processing perspective on hyperspectral unmixing: Insights from remote sensing
 IEEE Signal Processing Magazine
, 2014
"... Blind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most prominent research topics in signal processing for hyperspectral remote sensing [1, 2]. Blind HU aims at identifying materials present in a captured scene, ..."
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Cited by 14 (7 self)
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Blind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most prominent research topics in signal processing for hyperspectral remote sensing [1, 2]. Blind HU aims at identifying materials present in a captured scene,
MUSICCSR: Hyperspectral Unmixing via Multiple Signal Classification and Collaborative Sparse Regression
"... Spectral unmixing aims at finding the spectrally pure constituent materials (also called endmembers) and their respective fractional abundances in each pixel of a hyperspectral image scene. In recent years, sparse unmixing has been widely used as a reliable spectral unmixing methodology. In this app ..."
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Cited by 3 (2 self)
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Spectral unmixing aims at finding the spectrally pure constituent materials (also called endmembers) and their respective fractional abundances in each pixel of a hyperspectral image scene. In recent years, sparse unmixing has been widely used as a reliable spectral unmixing methodology. In this approach, the observed spectral vectors are expressed as linear combinations of spectral signatures assumed to be known a priori and present in a large collection, termed spectral library or dictionary, usually acquired in laboratory. Sparse unmixing has attracted much attention as it sidesteps two common limitations of classic spectral unmixing approaches: the lack of pure pixels in hyperspectral scenes and the need to estimate the number of endmembers in a given scene, which are very difficult tasks. However, the high mutual coherence of spectral libraries, jointly with their evergrowing dimensionality, strongly limits the operational applicability of sparse unmixing. In this paper, we introduce a twostep algorithm aimed at mitigating the aforementioned limitations. The algorithm exploits the usual low dimensionality of the hyperspectral data sets. The first step, similar to the multiple signal classification (MUSIC) array signal processing algorithm, identifies a subset of the library elements which contains the endmember signatures. Because this subset has cardinality much smaller than the initial number of library elements, the sparse regression we are led to is much more wellconditioned than the initial one using the complete library. The second step applies collaborative sparse regression (CSR), which is a form of structured sparse regression, exploiting the fact that only a few spectral signatures in the library are active. The effectiveness of the proposed approach, termed MUSICCSR, is extensively validated using both simulated and real hyperspectral data sets.
SelfDictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search Are Related
"... Abstract—This paper considers a recently emerged hyperspectral unmixing formulation based on sparse regression of a selfdictionary multiple measurement vector (SDMMV) model, wherein the measured hyperspectral pixels are used as the dictionary. Operating under the pure pixel assumption, this SD ..."
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Abstract—This paper considers a recently emerged hyperspectral unmixing formulation based on sparse regression of a selfdictionary multiple measurement vector (SDMMV) model, wherein the measured hyperspectral pixels are used as the dictionary. Operating under the pure pixel assumption, this SDMMV formalism is special in that it allows simultaneous identification of the endmember spectral signatures and the number of endmembers. Previous SDMMV studies mainly focus on convex relaxations. In this study, we explore the alternative of greedy pursuit, which generally provides efficient and simple algorithms. In particular, we design a greedy SDMMV algorithm using simultaneous orthogonal matching pursuit. Intriguingly, the proposed greedy algorithm is shown to be closely related to some existing pure pixel search algorithms, especially, the successive projection algorithm (SPA). Thus, a link between SDMMV and pure pixel search is revealed. We then perform exact recovery analyses, and prove that the proposed greedy algorithm is robust to noiseincluding its identification of the (unknown) number of endmembersunder a sufficiently low noise level. The identification performance of the proposed greedy algorithm is demonstrated through both synthetic and realdata experiments. Index Terms—Greedy pursuit, hyperspectral unmixing, number of endmembers estimation, selfdictionary sparse regression.
CollaborativeRepresentationBased Nearest Neighbor Classifier for Hyperspectral Imagery
"... Abstract—Novel collaborative representation (CR)based nearest neighbor (NN) algorithms are proposed for hyperspectral image classification. The proposed methods are based on a CR computed by an 2norm minimization with a Tikhonov regularization matrix. More specific, a testing sample is represent ..."
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Abstract—Novel collaborative representation (CR)based nearest neighbor (NN) algorithms are proposed for hyperspectral image classification. The proposed methods are based on a CR computed by an 2norm minimization with a Tikhonov regularization matrix. More specific, a testing sample is represented as a linear combination of all the training samples, and the weights for representation are estimated by an 2norm minimizationderived closedform solution. In the first strategy, the label of a testing sample is determined by majority voting of those with k largest representation weights. In the second strategy, local withinclass CR is considered as an alternative, and the testing sample is assigned to the class producing the minimum representation residual. The experimental results show that the proposed algorithms achieve better performance than several previous algorithms, such as the original kNN classifier and the local meanbased NN classifier. Index Terms—Collaborative representation (CR), hyperspectral data, nearest neighbors (NNs), pattern classification. I.
MULTIPLE GRAPH REGULARIZED NMF FOR HYPERSPECTRAL UNMIXING
"... Hyperspectral unmixing is an important technique for estimating fraction of different land covers from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) methods with various constraints have been introduced into hyperspectral unmixing. Among these methods, graph based ..."
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Hyperspectral unmixing is an important technique for estimating fraction of different land covers from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) methods with various constraints have been introduced into hyperspectral unmixing. Among these methods, graph based constraint has been proved to be useful in capturing the latent manifold structure of the hyperspectral data in the feature domain. However, due to the complexity of the data, only using single graph can not adequately reflect the intrinsic property of the data. In this paper, we propose a multiple graph regularized NMF method for hyperspectral unmixing, which approximates the manifold and consistency of data by a linear combination of several graphs constructed in different scales. Results on both synthetic and real data have validated the effectiveness of the proposed method, and shown that it has outperformed several stateofthearts hyperspectral unmixing methods.
unknown title
, 2014
"... 1Collaborative sparse regression using spatially correlated supports – Application to hyperspectral unmixing ..."
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1Collaborative sparse regression using spatially correlated supports – Application to hyperspectral unmixing
A GRAPH LAPLACIAN REGULARIZATION FOR HYPERSPECTRAL DATA UNMIXING
"... This paper introduces a graph Laplacian regularization in the hyperspectral unmixing formulation. The proposed regularization relies upon the construction of a graph representation of the hyperspectral image. Each node in the graph represents a pixel’s spectrum, and edges connect spectrally and spa ..."
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This paper introduces a graph Laplacian regularization in the hyperspectral unmixing formulation. The proposed regularization relies upon the construction of a graph representation of the hyperspectral image. Each node in the graph represents a pixel’s spectrum, and edges connect spectrally and spatially similar pixels. The proposed graph framework promotes smoothness in the estimated abundance maps and collaborative estimation between homogeneous areas of the image. The resulting convex optimization problem is solved using the Alternating Direction Method of Multipliers (ADMM). A special attention is given to the computational complexity of the algorithm, and Graphcut methods are proposed in order to reduce the computational burden. Finally, simulations conducted on synthetic data illustrate the effectiveness of the graph Laplacian regularization with respect to other classical regularizations for hyperspectral unmixing. Index Terms — Hyperspectral imaging, unmixing, graph Laplacian regularization, ADMM, sparse regularization.