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Proceedings Paper

Selecting training images with support vector machines for composite correlation filters in SAR ATR
Author(s): Daniel W. Carlson; Jack G. Riddle; Donald E. Waagen
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Paper Abstract

The focus of this paper is a genetic algorithms based method to automate the construction of local feature based composite class models that capture the salient characteristics of configuration variants of vehicle targets in SAR imagery and increase the performance of SAR recognition systems. The recognition models are based on quasi-invariant local features, SAR scattering center locations and magnitudes. The approach uses an efficient SAR recognition system as an evaluation function to determine the fitness of candidate members of a genetic population of new models and synthetically generates composite class models that are more similar to existing configurations than those configurations are to each other. Intuitively, specific features of models of versions A and B of an object may not match, because they are outside of some tolerance, while they may both match some synthetic version C that is somewhere in the middle. Experimental recognition results are presented in terms of receiver operating characteristic (ROC) curves to show the improvements in SAR recognition performance utilizing composite class models of configuration variants of MSTAR vehicle targets.

Paper Details

Date Published: 12 September 2003
PDF: 11 pages
Proc. SPIE 5095, Algorithms for Synthetic Aperture Radar Imagery X, (12 September 2003); doi: 10.1117/12.487433
Show Author Affiliations
Daniel W. Carlson, Raytheon Co. (United States)
Jack G. Riddle, Raytheon Co. (United States)
Donald E. Waagen, Raytheon Co. (United States)

Published in SPIE Proceedings Vol. 5095:
Algorithms for Synthetic Aperture Radar Imagery X
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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