Spectral-spatial constrained sparse unmixing of hyperspectral imagery using a hybrid spectral library

ABSTRACTSpectral unmixing based on sparse regression model has recently attracted the hyperspectral data processing community. The technology has two important characteristics. First, sparseness of the abundance matrix is induced because the number of materials in a mixed pixel is very small when compared with the signatures in spectral library. Second, constraints are applied to improve the reconstruction accuracy of the optimization problem. In this letter, a spectral and spatial constrained sparse unmixing (SSCSUn) method is proposed, in which the total variation (TV) regularization with a spatial weight factor is incorporated to exploit spatial information more effectively. Furthermore, several endmembers from the real image are used to form a new hybrid spectral library and are applied as spectral a priori constraints. Experimental results on simulated and real hyperspectral images demonstrate the effectiveness of the proposed approach.