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• W2963265857 abstract "The goal of hyperspectral unmixing 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 based on loopy belief propagation (BP) that enables the exploitation of spectral coherence in the endmembers and spatial coherence in the abundances. In particular, we partition the factor graph into spectral coherence, spatial coherence, and bilinear subgraphs, and pass messages between them using a “turbo” approach. To perform message passing within the bilinear subgraph, we employ the bilinear generalized approximate message passing algorithm (BiG-AMP), a recently proposed belief-propagation-based approach to matrix factorization. Furthermore, we propose an expectation-maximization (EM) strategy to tune the prior parameters and a model-order selection strategy to select the number of materials $N$ . Numerical experiments conducted with both synthetic and real-world data show favorable unmixing performance relative to existing methods." @default.
• W2963265857 created "2019-07-30" @default.
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• W2963265857 creator A5041541438 @default.
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• W2963265857 date "2015-09-01" @default.
• W2963265857 modified "2023-10-16" @default.
• W2963265857 title "Hyperspectral Unmixing Via Turbo Bilinear Approximate Message Passing" @default.
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• W2963265857 doi "https://doi.org/10.1109/tci.2015.2465161" @default.
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