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Proceedings Paper

Endmember generation by projection pursuit
Author(s): Gregory Solyar; Chein-I Chang; Antonio Plaza
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Paper Abstract

Projection pursuit (PP) is an interesting concept, which has been found in many applications. It uses a so-called projection index (PI) as a criterion to seek directions that may lead to interesting findings for data analysts. Unlike the principal components analysis (PCA), which uses variance as a measure to find directions that maximizes data variances, the PI used by the PP finds interesting directions that can be characterized by statistics higher than variance. As a result, the PCA is generally considered as a special case of PP with the PI particularly specified by the variance. Recently, a PP-based approach was developed by Ifarraguerri and Chang for multispectral/hyperspectral image analysis. This paper revisits their approach and investigates its application in endmember generation where endmembers can be extracted from a sequence of projections generated by PP.

Paper Details

Date Published: 1 June 2005
PDF: 10 pages
Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.604137
Show Author Affiliations
Gregory Solyar, Univ. of Maryland/Baltimore County (United States)
Chein-I Chang, Univ. of Maryland/Baltimore County (United States)
Antonio Plaza, Univ. of Maryland/Baltimore County (United States)
Univ. of Extremadura (Spain)


Published in SPIE Proceedings Vol. 5806:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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