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Proceedings Paper

The impact of initialization procedures on unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization
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Paper Abstract

The authors proposed in previous papers the use of the constrained Positive Matrix Factorization (cPMF) to perform unsupervised unmixing of hyperspectral imagery. Two iterative algorithms were proposed to compute the cPMF based on the Gauss-Seidel and penalty approaches to solve optimization problems. Results presented in previous papers have shown the potential of the proposed method to perform unsupervised unmixing in HYPERION and AVIRIS imagery. The performance of iterative methods is highly dependent on the initialization scheme. Good initialization schemes can improve convergence speed, whether or not a global minimum is found, and whether or not spectra with physical relevance are retrieved as endmembers. In this paper, different initializations using random selection, longest norm pixels, and standard endmembers selection routines are studied and compared using simulated and real data.

Paper Details

Date Published: 7 May 2007
PDF: 11 pages
Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65650B (7 May 2007); doi: 10.1117/12.719779
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. 6565:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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