An Antinoise Method for Hyperspectral Unmixing

Abstract

In this letter, we propose an antinoise method for hyperspectral unmixing. In the antinoise method, all noises are addressed. The following techniques are applied: 1) an endmember dictionary is constructed first to initialize the solution; 2) an approximated L 0 norm constraint is employed to prune the dictionary and fulfill the sparse coding; and 3) the Itakura-Saito divergence, instead of the Square of Euclidean Distance divergence, is utilized to construct a novel optimization function. The experimental results on both synthetic and real hyperspectral data sets demonstrate the efficacy of the proposed method.