Futuristic greedy approach to sparse unmixing of hyperspectral data

Abstract

Spectra measured at a single pixel of a remotely sensed hyperspectral image is usually a mixture of multiple spectral signatures (endmembers) corresponding to different materials on the ground. Sparse unmixing assumes that a mixed pixel is a sparse linear combination of different spectra already available in a spectral library. It uses sparse approximation (SA) techniques to solve the hyperspectral unmixing problem. Among these techniques, greedy algorithms suite well to sparse unmixing. However, their accuracy is immensely compromised by the high correlation of the spectra of different materials. This paper proposes a novel greedy algorithm, called OMP-Star, that shows robustness against th..