Priors in sparse recursive decompositions of hyperspectral images

Nicolas Gillis; Robert J. Plemmons; Qiang Zhang

doi:10.1117/12.918333

24 May 2012 Priors in sparse recursive decompositions of hyperspectral images

Nicolas Gillis, Robert J. Plemmons, Qiang Zhang

Proceedings Volume 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII; 83901M (2012) https://doi.org/10.1117/12.918333
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Nonnegative matrix factorization and its variants are powerful techniques for the analysis of hyperspectral images (HSI). Nonnegative matrix underapproximation (NMU) is a recent closely related model that uses additional underapproximation constraints enabling the extraction of features (e.g., abundance maps in HSI) in a recursive way while preserving nonnegativity. We propose to further improve NMU by using the spatial information: we incorporate into the model the fact that neighboring pixels are likely to contain the same materials. This approach thus incorporates structural and textural information from neighboring pixels. We use an ℓ₁-norm penalty term more suitable to preserving sharp changes, and solve the corresponding optimization problem using iteratively reweighted least squares. The effectiveness of the approach is illustrated with analysis of the real-world cuprite dataset.

Citation Download Citation

Nicolas Gillis, Robert J. Plemmons, and Qiang Zhang "Priors in sparse recursive decompositions of hyperspectral images", Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901M (24 May 2012); https://doi.org/10.1117/12.918333

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