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Proceedings Paper

Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization
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Paper 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.

Paper Details

Date Published: 17 April 2006
PDF: 9 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470B (17 April 2006); doi: 10.1117/12.667976
Show Author Affiliations
Yahya M. Masalmah, Univ. of Puerto Rico, Mayagüez (United States)
Miguel Vélez-Reyes, Univ. of Puerto Rico, Mayagüez (United States)


Published in SPIE Proceedings Vol. 6247:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Harold H. Szu, Editor(s)

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