Fully constrained linear spectral unmixing algorithm for hyperspectral image analys

In this article, a novel linear spectral unmixing algorithm is proposed and analyzed. The linear spectral mixture defines a model of pixels for hyperspectral images by means of spectral signatures. A set of spectral signatures is assumed to be known. Constraints are imposed on the spectral mixture coefficients: the sum of the coefficients is equal to unity and each coefficient is nonnegative. The results of the algorithm quality and speed analysis are described in the paper.