Endmember extraction using a combination of orthogonal projection and genetic algorithm

Abstract

Common endmember extraction algorithms presume that the number of materials present is either known or may be predetermined by using spectral databases or other approaches. In this letter, we propose a new method called genetic orthogonal projection (GOP) for endmember extraction in imaging spectrometry. GOP is based on a fully unsupervised approach and uses convex geometric characteristics as well as a genetic algorithm. We compare GOP with existing endmember extraction algorithms and demonstrate that GOP partially outperforms them, without the need of a priori information.