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Proceedings Paper

HRR signature classification using a hybrid composite classification system
Author(s): Michael A. Turnbaugh; Kenneth W. Bauer
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Paper Abstract

We propose several fusion techniques in the design of a hybrid composite classification system. Our composite classifier taps into the strengths of two separate classification paradigms and examines various fusion methods for combining the two. The first classifier uses non-numeric features, similar to those found in syntactic pattern recognition, by exploiting the overall structure of the patterns themselves. The second method uses a more classical feature vector method that bins the patterns and uses the maximum values within each bin in developing the feature vector for each pattern. By using these two separate approaches, we explore conditions that allow the two techniques to be complementary in nature, thus improving, when fused, the overall performance of the classification system. We examine four seperate fusion techniques, the Basic Ensemble Method, the Probabilistic Neural Network, the Borda Count and the Bayesian Belief Network using a ten class problem in our experiments.

Paper Details

Date Published: 14 April 2008
PDF: 11 pages
Proc. SPIE 6967, Automatic Target Recognition XVIII, 696716 (14 April 2008); doi: 10.1117/12.781555
Show Author Affiliations
Michael A. Turnbaugh, Air Force Institute of Technology (United States)
Kenneth W. Bauer, Air Force Institute of Technology (United States)

Published in SPIE Proceedings Vol. 6967:
Automatic Target Recognition XVIII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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