Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization

Yahya M. Masalmah; Miguel Vélez-Reyes

doi:10.1117/12.667976

17 April 2006 Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization

Proceedings Volume 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV; 62470B (2006) https://doi.org/10.1117/12.667976
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

Yahya M. Masalmah and 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); https://doi.org/10.1117/12.667976

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