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Proceedings Paper

Priors in sparse recursive decompositions of hyperspectral images
Author(s): Nicolas Gillis; Robert J. Plemmons; Qiang Zhang
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Paper 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.

Paper Details

Date Published: 24 May 2012
PDF: 12 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901M (24 May 2012); doi: 10.1117/12.918333
Show Author Affiliations
Nicolas Gillis, Univ. of Waterloo (Canada)
Robert J. Plemmons, Wake Forest Univ. (United States)
Qiang Zhang, Wake Forest Univ. Health Sciences (United States)

Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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