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1 CRAN - Centre de Recherche en Automatique de Nancy 2 LCPME - Laboratoire de Chimie Physique et Microbiologie pour l-Environnement

Abstract : This paper presents a new technique for hyperspectral images classification based on simultaneous sparse approximation. The proposed approach consists in formulating the problem as a convex multi-objective optimization problem which incorporates a term favoring the simultaneous sparsity of the estimated coefficients and a term enforcing a regularity constraint along the rows of the coefficient matrix. We show that the optimization problem can be solved efficiently using FISTA Fast Iterative Shrinkage-Thresholding Algorithm. This approach is applied to a wood wastes classification problem using NIR hyperspectral images.

Keywords : Hyperspectral image classification simultaneous sparse approximation regularization constraint FISTA





Autor: Leila Belmerhnia - El-Hadi Djermoune - David Brie - Cédric Carteret -

Fuente: https://hal.archives-ouvertes.fr/



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