Sparse hyperspectral unmixing based on smoothed l0 regularization

Sparse based approach has recently received much attention in hyperspectral unmixing area. Sparse unmixing is based on the assumption that each measured pixel in the hyperspectral image can be expressed by a number of pure spectra linear combination from a spectral library known in advance. Despite the success of sparse unmixing based on the l(0) or l(1) regularizer, the limitation of this approach on its computational complexity or sparsity affects the efficiency or accuracy. As the smoothed l(0) regularizer is much easier to solve than the l(0) regularizer and has stronger sparsity than the l(1) regularizer, in this paper, we choose the smoothed l(0) norm as an alternative regularizer and model the hyperspectral unmixing as a constrained smoothed l(0) - l(2) optimization problem, namely SL0SU algorithm. We then use the variable splitting augmented Lagrangian algorithm to solve it. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed SL0SU is much more effective and accurate on hyperspectral unmixing than the state-of-the-art SUnSAL method. (C) 2014 Elsevier B.V. All rights reserved.