On the acceleration of the N-FINDER algorithm for hyperspectral endmembers extraction

Abstract

This paper presents a new method in order to perform the endmembers extraction with the same accuracy in the results that the well known Winter's N-Finder algorithm but with less computational effort. In particular, our proposal makes use of the Orthogonal Subspace Projection algorithm, OSP, as well as the information provided by the dimensionality reduction step that takes place prior to the endmembers extraction itself. The results obtained using the proposed methodology demonstrate that more than half of the computing time is saved with negligible variations in the quality of the endmembers extracted, compared with the results obtained with the Winter's N-Finder algorithm. Moreover, this is achieved with independence of the amount of noise and/or the number of endmembers of the hyperspectral image under processing.