An algorithm for unsupervised unmixing of hyperspectral imagery using positive matrix factorization

Yahya M. Masalmah; Miguel Velez-Reyes; Samuel Rosario-Torres

doi:10.1117/12.605672

1 June 2005 An algorithm for unsupervised unmixing of hyperspectral imagery using positive matrix factorization

Yahya M. Masalmah, Miguel Velez-Reyes, Samuel Rosario-Torres

Proceedings Volume 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI; (2005) https://doi.org/10.1117/12.605672
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

This paper presents an approach for simultaneous determination of endmembers and their abundances in hyperspectral imagery using a constrained positive matrix factorization. The algorithm presented here solves the constrained PMF by formulating it as a nonnegative least squares problem where the cost function is expanded with a penalty term to enforce the sum to one constraint. Preliminary results using simulated and AVIRIS-Cuprite data are presented. These results show the potential of the method to solve the unsupervised unmixing problem.

Citation Download Citation

Yahya M. Masalmah, Miguel Velez-Reyes, and Samuel Rosario-Torres "An algorithm for unsupervised unmixing of hyperspectral imagery using positive matrix factorization", Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); https://doi.org/10.1117/12.605672

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