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Proceedings Paper

Application of nonnegative principal component analysis in hyperspectral imaging
Author(s): Peter Bajorski
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Paper Abstract

The classic PCA (Principal Component Analysis) has been applied in hyperspectral imaging with varying success. One obstacle in its application is the potential physical interpretation of the principal components, which is questionable unless the principal component coefficients are nonnegative. In this paper, we show hyperspectral imaging applications of a recently developed methodology of nonnegative PCA, which overcomes this difficulty by constructing nonnegative principal components. We construct an approximation of a physics-derived target space, and suggest some interpretations of the resulting components.

Paper Details

Date Published: 1 September 2006
PDF: 8 pages
Proc. SPIE 6302, Imaging Spectrometry XI, 63020G (1 September 2006); doi: 10.1117/12.677375
Show Author Affiliations
Peter Bajorski, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 6302:
Imaging Spectrometry XI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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