Dimensionality reduction, classification, and spectral mixture analysis using nonnegative underapproximation

Nicolas Gillis; Robert J. Plemmons

doi:10.1117/12.849345

22 April 2010 Dimensionality reduction, classification, and spectral mixture analysis using nonnegative underapproximation

Nicolas Gillis, Robert J. Plemmons

Author Affiliations +

Proceedings Volume 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI; 76951A (2010) https://doi.org/10.1117/12.849345
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Nonnegative Matrix Factorization (NMF) and its variants have recently been successfully used as dimensionality reduction techniques for identification of the materials present in hyperspectral images. In this paper, we present a new variant of NMF called Nonnegative Matrix Underapproximation (NMU): it is based on the introduction of underapproximation constraints which enables one to extract features in a recursive way, like PCA, but preserving nonnegativity. Moreover, we explain why these additional constraints make NMU particularly wellsuited to achieve a parts-based and sparse representation of the data, enabling it to recover the constitutive elements in hyperspectral data. We experimentally show the efficiency of this new strategy on hyperspectral images associated with space object material identification, and on HYDICE and related remote sensing images.

Citation Download Citation

Nicolas Gillis and Robert J. Plemmons "Dimensionality reduction, classification, and spectral mixture analysis using nonnegative underapproximation", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76951A (22 April 2010); https://doi.org/10.1117/12.849345

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