Unmixing Sparse Hyperspectral Mixtures

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Unmixing Sparse Hyperspectral Mixtures
Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or atoms), is a hard combinatorial problem which, as in other areas, has been increasingly addressed. This paper studies the efficiency of the sparse regression techniques in the spectral unmixing problem by conducting a comparison between four different approaches: Moore-Penrose Pseudoinverse, Orthogonal Matching Pursuit (OMP) [1], Iterative Spectral Mixture Analysis (ISMA) [2] and 12 ll - sparse regression techniques, which are of widespread use in compressed sensing. We conclude that the 12 ll - sparse regression techniques, implemented here by Iterative Shrinkage/Thresholding (TwIST) algorithm [3], yield the state-ofthe-art in the hyperspectral unmixing area.
Marian-Daniel Iordache, José M. Bioucas-Dia
Added 20 Feb 2011
Updated 20 Feb 2011
Type Journal
Year 2009
Authors Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio Plaza
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