Robust linear unmixing with enhanced sparsity
Résumé
Spectral unmixing is a central problem in hyperspec-tral imagery. It is usually assuming a linear mixture model. Solving this inverse problem, however, can be seriously impacted by a wrong estimation of the number of endmembers, a bad estimation of the endmembers themselves, the spectral variability of the endmembers or the presence of nonlinearities. These problems can result in a too large number of retained endmembers. We propose to tackle this problem by introducing a new formulation for robust linear unmixing enhancing spar-sity. With a single tuning parameter the optimization leads to a range of behaviors: from the standard linear model (low sparsity) to a hard classification (maximal sparsity : only one endmember is retained per pixel). We solve the proposed new functional using a compu-tationally efficient proximal primal dual method. The experimental study, including both realistic simulated data and real data demonstrates the versatility of the proposed approach.
Origine : Fichiers produits par l'(les) auteur(s)
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