Priors in Sparse Recursive Decompositions of Hyperspectral Images
(2012) SPIE — Location: Baltimore, Maryland (23.April.2012)
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- 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 ℓ<sub>1</sub>-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.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Citations
Gillis, N., Plemmons, R. J., & Zhang, Q. (2012). Priors in Sparse Recursive Decompositions of Hyperspectral Images. In Sylvia S. Shen, Paul E. Lewis (Eds) (ed.), Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. https://doi.org/10.1117/12.918333