18 May 2013 Hyperspectral image unmixing via bilinear generalized approximate message passing
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In hyperspectral unmixing, the objective is to decompose an electromagnetic spectral dataset measured over M spectral bands and T pixels, into N constituent material spectra (or “endmembers”) with corresponding spatial abundances. In this paper, we propose a novel approach to hyperspectral unmixing (i.e., joint estimation of endmembers and abundances) based on loopy belief propagation. In particular, we employ the bilinear generalized approximate message passing algorithm (BiG-AMP), a recently proposed belief-propagation-based approach to matrix factorization, in a “turbo” framework that enables the exploitation of spectral coherence in the endmembers, as well as spatial coherence in the abundances. In conjunction, we propose an expectation- maximization (EM) technique that can be used to automatically tune the prior statistics assumed by turbo BiG-AMP. Numerical experiments on synthetic and real-world data confirm the state-of-the-art performance of our approach.
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Jeremy Vila, Jeremy Vila, Philip Schniter, Philip Schniter, Joseph Meola, Joseph Meola, } "Hyperspectral image unmixing via bilinear generalized approximate message passing", Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87430Y (18 May 2013); doi: 10.1117/12.2015859; https://doi.org/10.1117/12.2015859

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