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Subspace matching pursuit with Dice coefficient for sparse unmixing of hyperspectral data

Sparse unmixing is a popular linear spectral unmixing tool in remotely sensed hyperspectral data interpretation. It can be worked out in semisupervised fashion by taking the advantage of the spectral library known in advance. Most sparse regressions methods are based on convex relaxation methods which try to obtain the global solution of a well-defined optimization problem. Recently, more and more attention has been paid to greedy algorithms for sparse unmixing because of their low complexity. Among of them, subspace matching pursuit (SMP) is a preferable one to recover the optimal endmembers according to the referred subspace of the original image. In this paper, we study the linear spectral unmixing problem under the method of SMP. Furthermore, in order to increase the robust in the case of low SNR, we provide a novel algorithm to replace the inner product of the matching measurement criteria of sparse representation with the Dice coefficient to improve the unmixing accuracy.