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Proceedings Paper

Kernel canonical correlation analysis for hyperspectral anomaly detection
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Paper Abstract

In this paper, we present a kernel-based nonlinear version of canonical correlation analysis (CCA), so called kernel canonical correlation analysis (KCCA), for hyperspectral anomaly detection applications. CCA only measures linear dependency between two sets of signal vectors (target and background) ignoring higher order correlations crucial for distinguishing between man-made objects and background clutter. In order to exploit nonlinear correlations we implicitly map the two sets of data into a high dimensional feature space where correlations of nonlinear features extracted from the original data are exploited by a kernel function. A generalized eigenproblem is then formulated for KCCA. In this paper, both CCA and KCCA are applied to real hyperspectral images and detection performance of CCA and KCCA are compared to the well-known RX anomaly detection algorithm.

Paper Details

Date Published: 4 May 2006
PDF: 8 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 623303 (4 May 2006); doi: 10.1117/12.664112
Show Author Affiliations
Heesung Kwon, Army Research Lab. (United States)
Nasser M. Nasrabadi, Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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