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Proceedings Paper

Improving ATR performance through distance metric learning
Author(s): Yijun Sun; Ming Xue; Jian Li; S. Robert Stanfill
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Paper Abstract

High resolution synthetic aperture radar images usually contain much redundant, noisy and irrelevant information. Eliminating these information or extracting only useful information can enhance ATR performance, reduce processing time and increase the robustness of the ATR systems. Most existing feature extraction methods are either computationally expensive or can only provide ad hoc solutions and have no guarantee of optimality. In this paper, we describe a new distance metric learning algorithm. The algorithm is based on the local learning strategy and is formulated as a convex optimization problem. The algorithm not only is capable of learning the feature significance and feature correlations in a high dimensional space but also is very easy to implement with guaranteed global optimality. Experimental results based on the MSTAR database are presented to demonstrate the effectiveness of the new algorithm.

Paper Details

Date Published: 7 May 2007
PDF: 8 pages
Proc. SPIE 6568, Algorithms for Synthetic Aperture Radar Imagery XIV, 65680S (7 May 2007); doi: 10.1117/12.714183
Show Author Affiliations
Yijun Sun, Univ. of Florida (United States)
Ming Xue, Univ. of Florida (United States)
Jian Li, Univ. of Florida (United States)
S. Robert Stanfill, Lockheed Martin Missiles and Fire Control (United States)

Published in SPIE Proceedings Vol. 6568:
Algorithms for Synthetic Aperture Radar Imagery XIV
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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