17 April 2006 Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization
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Abstract
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.
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Yahya M. Masalmah, Yahya M. Masalmah, Miguel Vélez-Reyes, Miguel Vélez-Reyes, } "Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization", Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470B (17 April 2006); doi: 10.1117/12.667976; https://doi.org/10.1117/12.667976
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