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PIXEL-WISE LINEAR/NONLINEAR NONNEGATIVE MATRIX FACTORIZATION FOR UNMIXING OF HYPERSPECTRAL DATA

Fei Zhu 1 Paul Honeine 2 Jie Chen 3
1 Center for Applied Mathematics, Tianjin University
2 DocApp - LITIS - Equipe Apprentissage
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
3 School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China
Abstract : Nonlinear spectral unmixing is a challenging and important task in hyperspectral image analysis. The kernel-based bi-objective nonnegative matrix factorization (Bi-NMF) has shown its usefulness in nonlinear unmixing; However, it suffers several issues that prohibit its practical application. In this work, we propose an unsupervised nonlinear unmixing method that overcomes these weaknesses. Specifically, the new method introduces into each pixel a parameter that adjusts the nonlinearity therein. These parameters are jointly optimized with endmembers and abundances, using a carefully designed objective function by multiplicative update rules. Experiments on synthetic and real datasets confirm the effectiveness of the proposed method.
Keywords : Hyperspectral data analysis nonlinear unmixing unsupervised learning kernel methods nonnegative matrix factorization
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Conference papers
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https://hal-normandie-univ.archives-ouvertes.fr/hal-03088297
Contributor : Paul Honeine <>
Submitted on : Saturday, December 26, 2020 - 12:39:20 AM
Last modification on : Wednesday, January 13, 2021 - 3:38:26 AM

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Fei Zhu, Paul Honeine, Jie Chen. PIXEL-WISE LINEAR/NONLINEAR NONNEGATIVE MATRIX FACTORIZATION FOR UNMIXING OF HYPERSPECTRAL DATA. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2020, Barcelona, Spain. pp.4737-4741, ⟨10.1109/ICASSP40776.2020.9053239⟩. ⟨hal-03088297⟩

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