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Published Articles >> Table of Contents >> Abstract
32nd Applied Imagery Pattern Recognition Workshop (AIPR'03)
p. 27
Fusion Techniques for Automatic Target Recognition
Syed A. Rizvi, College of Staten Island of City University of New York, Staten Island, NY
Nasser M. Nasrabadi, U.S.Army Research Laboratory, Adelphi, MD
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AIPR.2003.1284244
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| Abstract |
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In this paper, we investigate several fusion techniques for
designing a composite classifier to improve the
performance (probability of correct classification) of FLIR
ATR. In this research, we propose to use four ATR
algorithms for fusion. The individual performance of the
four contributing algorithms ranges from 73.5% to about
77% of probability of correct classification on the testing
set. We propose to use Bayes classifier, committee of
experts, stacked-generalization, winner-takes-all, and
ranking-based fusion techniques for designing the
composite classifiers. The experimental results show an
improvement of more than 6.5% over the best individual
performance.
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 Additional Information
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Citation:
Syed A. Rizvi, Nasser M. Nasrabadi,
"Fusion Techniques for Automatic Target Recognition,"
aipr,
p. 27,
32nd Applied Imagery Pattern Recognition Workshop (AIPR'03),
2003
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