Sparse pixel-wise spectral unmixing — Which algorithm to use and how to improve the results

Abstract

Recently, many sparse approximation methods have been applied to solve spectral unmixing problems. These methods in contrast to traditional methods for spectral unmixing are designed to work with large a-prori given spectral dictionaries containing hundreds of labelled material spectra enabling to skip the expensive endmember extraction and labelling step. However, it has been shown that sparse approximation methods sometimes have problems with selection of correct spectra from the dictionary when these are similar. In this paper we study the detection and approximation accuracy of different sparse approximation methods as well as the influence of the proposed modifications.