Robust Linear Unmixing with Enhanced Sparsity

Alexandre Tiard, Laurent Condat, Lucas Drumetz, Jocelyn Chanussot, Wotao Yin, and Xiaoxian Zhu

Published in ICIP’17


Spectral unmixing is a central problem in hyperspectral 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 sparsity. 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 computationally efficient proximal primal dual method. The experimental study, including both realistic simulated data and real data demonstrates the versatility of the proposed approach.


A. Tiard, L. Condat, L. Drumetz, J. Chanussot, W. Yin, and X. Zhu, Robust linear unmixing with enhanced sparsity, Image Processing (ICIP), 2017 IEEE International Conference on, Beijing, 2017. DOI: 10.1109/ICIP.2017.8296861

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