On the use of overcomplete dictionaries for spectral unmixing

Hyperspectral unmixing is a sub pixel classification method which aims at recovering fraction and type of materials mixed in a single pixel. This work addresses the unmixing problem from the compressive sensing point of view by using overcomplete dictionaries enabling automatization of the process. However, overcomplete dictionaries of spectra are highly coherent which might confuse the final unmixing result. To deal with this problem we propose the use of differentiated spectra for coherence reduction. In this paper we study the approximation error for the proposed method as well as the correctness of the material detection.