Local Sparse Representation Based Spatial Preprocessing For Endmember Extraction

Abstract

Hyperspectral unmixing has been widely used to decompose a mixed pixel into a collection of endmembers weighted by their corresponding fractional abundances, in which endmember extraction step is of crucial importance. Many classical endmember extraction algorithms mainly identify spectrally pure endmembers according to spectra of pixels, e.g., NFINDR and vertex component analysis (VCA), ignoring spatial distribution or structure information that has been demonstrated to be complemental for spectral information in hyperspectral image processing. In order to improve the performance of these classical endmember extraction algorithms, a novel spatial preprocessing method is proposed to explore spatial information prior to endmember extraction step. Specifically, pixels in hyperspectral images are modified using their sparse linear approximation by neighboring pixels, such that spectral variation within a local spatial neighbor-hood can be alleviated. Experimental results on both simulated and real data sets demonstrate that the proposed local sparse representation based spatial preprocessing algorithm is capable of producing better unmixing result compared to several state-of-the-art spatial preprocessing methods.