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Communication Dans Un Congrès Année : 2017

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.
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Dates et versions

hal-01656640 , version 1 (03-06-2019)

Identifiants

Citer

Alexandre Tiard, Laurent Condat, Lucas Drumetz, Jocelyn Chanussot, Wotao Yin, et al.. Robust linear unmixing with enhanced sparsity. ICIP 2017 - 24th IEEE International Conference on Image Processing, Sep 2017, Beijing, China. ⟨10.1109/ICIP.2017.8296861⟩. ⟨hal-01656640⟩
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