A regularized sparse approximation method for hyperspectral image classification

Leila Belmerhnia, El-Hadi Djermoune, David Brie, Cedric Carteret
2016 2016 IEEE Statistical Signal Processing Workshop (SSP)  
Cédric Carteret. 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
more » ... ient 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.
doi:10.1109/ssp.2016.7551846 dblp:conf/ssp/BelmerhniaDBC16 fatcat:2sbst4ps2jdfreouybf2ybye4u