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Proceedings Paper

Hyperspectral target detection using independent component analysis-based linear mixture model
Author(s): E. Sarigul; M. S. Alam
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Paper Abstract

This paper presents an Independent Component Analysis (ICA) based linear unmixing algorithm for target detection application. ICA is a relatively new method that attempts to separate statistically independent sources from a mixed dataset. The developed algorithm contains two steps. In the first step, ICA based linear unmixing is used to discriminate statistically independent sources to determine end-members in a given dataset as well as their corresponding abundance images. In the second step, unmixing results are analyzed to identify abundance images that correspond to the target class. The performance of the developed algorithm has been evaluated with several real life hyperspectral image datasets.

Paper Details

Date Published: 7 May 2007
PDF: 11 pages
Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65650A (7 May 2007); doi: 10.1117/12.720230
Show Author Affiliations
E. Sarigul, Univ. of South Alabama (United States)
M. S. Alam, Univ. of South Alabama (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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