Hyperspectral Unmixing Via Simultaneous Dictionary Refining and Enhanced Sparse Regression

Abstract

The dictionary-aided sparse regression (SR) approach has been developed in hyperspectral unmixing (HU) in remote sensing. By using an available spectral library as a dictionary, the SR approaches unmix the spectral image by selecting the endmember matrix from the library which best represent the image and its fractional abundance. In this paper, we proposed a simultaneous dictionary refining and enhanced sparse regression method for hyperspectral unmixing(DRESR). The proposed method not only enhances the sparsity of fractional abundance through double weighted sparse regularization, and also improves the spectral signature mismatches between an actual spectra and its corresponding endmember in the spectral library by using dictionary sparse refining. Experimental results on both synthetic and real hyperspectral data sets demonstrate better performance compared with several state-of-art algorithms.