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A regularized sparse approximation method for hyperspectral image classification

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.
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Submitted on : Monday, June 20, 2016 - 4:26:33 PM
Last modification on : Thursday, December 9, 2021 - 2:54:07 PM

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  • HAL Id : hal-01310059, version 2

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Leila Belmerhnia, El-Hadi Djermoune, David Brie, Cédric Carteret. A regularized sparse approximation method for hyperspectral image classification. 19th IEEE Workshop on Statistical Signal Processing, SSP 2016, Jun 2016, Palma de Majorque, Spain. ⟨hal-01310059v2⟩

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