Unsupervised unmixing of hyperspectral imagery using the positive matrix factorization

This paper presents an approach for simultaneous determination of endmembers and their abundances in hyperspectral imagery unmixing using a constrained positive matrix factorization (PMF). The algorithm presented here solves the constrained PMF using Gauss-Seidel method. This algorithm alternates between the endmembers matrix updating step and the abundance estimation step until convergence is achieved. Preliminary results using a subset of a HYPERION image taken in SW Puerto Rico are presented. These results show the potential of the proposed method to solve the unsupervised unmixing problem.