Dictionary-aided hyperspectral unmixing based on constrained l2,q-l2,p optimization

Dictionary-aided unmixing has been introduced as a semi-supervised unmixing method, under the assumption that the observed mixed pixel of a hyperspectral image can be expressed in the form of different linear combinations of a few spectral signatures from an available spectral library. Sparse-regression-based unmixing methods have been recently proposed to solve this problem. Mostly, lp-norm minimization is a closer surrogate to the l0-norm minimization and can be solved more efficiently than l1-norm minimization. In this paper, we model the hyperspectral unmixing as a constrained l2,q-l2,p optimization problem. To effectively solve the induced optimization problems for any q (1≤q≤2) and p (0