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Proceedings Paper

Determining training data requirements for template based normalized cross correlation
Author(s): Peter Knee; Lee Montagnino; Shawn Halversen; Andreas Spanias
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Paper Abstract

In this paper, we investigate the effect of increasingly sparse training data sets on target classification performance using a template-based classifier. An often used method of template creation employs averaging of multiple target training chips for a predefined coverage swath. The inclusion of too many training chips results in a blurring of the predominant scatterers while averaging of too few training chips results in poor edge resolution. We use the public MSTAR data set to show that using all appropriate images for each template may not result in the best ATR performance. We successfully demonstrate the ability to reduce training data collection requirements by requiring fewer training chips per template.

Paper Details

Date Published: 5 May 2009
PDF: 8 pages
Proc. SPIE 7335, Automatic Target Recognition XIX, 73350O (5 May 2009); doi: 10.1117/12.820063
Show Author Affiliations
Peter Knee, Arizona State Univ. (United States)
Lee Montagnino, Raytheon Missile Systems (United States)
Shawn Halversen, Raytheon Missile Systems (United States)
Andreas Spanias, Arizona State Univ. (United States)


Published in SPIE Proceedings Vol. 7335:
Automatic Target Recognition XIX
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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