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Proceedings Paper

Optimization of nonlinear kernel PCA feature extraction algorithms for automatic target recognition
Author(s): Seth Winger; Thomas Lu; Tien-Hsin Chao
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Paper Abstract

We present a multi-stage automatic target recognition (ATR) system using a kernel-based PCA (kPCA) for nonlinear feature extraction. The kPCA method uses a nonlinear kernel function to map data onto a higher dimensional space and then performs the PCA in the feature space. An algorithm for inserting kernel PCA into the existing ATR system was designed and various types of kernels were tested and optimized on several testing image sets such as video images of boats in choppy waves or approaching helicopters. We discuss the performance comparisons and trade-offs in using kPCA for ATR operations. kPCA generally outperforms normal PCA in classification accuracy and free-response receiver operating characteristics (FROC).

Paper Details

Date Published: 26 April 2011
PDF: 11 pages
Proc. SPIE 8055, Optical Pattern Recognition XXII, 80550E (26 April 2011); doi: 10.1117/12.886148
Show Author Affiliations
Seth Winger, Stanford Univ. (United States)
Thomas Lu, Jet Propulsion Lab. (United States)
Tien-Hsin Chao, Jet Propulsion Lab. (United States)

Published in SPIE Proceedings Vol. 8055:
Optical Pattern Recognition XXII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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