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Proceedings Paper

HRR ATR using VQ classification with a reject option
Author(s): Batuhan Ulug; Stanley C. Ahalt
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Paper Abstract

Automatic Target Recognition (ATR) systems are required to identify and differentiate between a large number of targets under a broad class of scenario variations. To accomplish this task ATR systems will employ high-dimensional data, such as High Range Resolution (HRR) radar data, which improves discriminability, but leads to very large databases and the attendant computational and storage requirements. Reducing the size of ATR databases without jeopardizing recognition performance is a potential solution to the above challenges. This reduction can be achieved through: (1) Feature Extraction, or (2) Vector Quantization. In this paper we apply VQ classification algorithms to measured HRR radar data to assess the effects of database compression on ATR performance. In particular, we introduce a distance-based reject option into the nearest neighbor classification scheme and perform experiments to investigate the error-reject tradeoff via error-reject curves. Experimental results indicate that a substantial compression (about 10:1) of the training database can be achieved with little degradation of ATR performance on the measured HRR database.

Paper Details

Date Published: 24 August 1999
PDF: 7 pages
Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999); doi: 10.1117/12.359959
Show Author Affiliations
Batuhan Ulug, The Ohio State Univ. (United States)
Stanley C. Ahalt, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 3718:
Automatic Target Recognition IX
Firooz A. Sadjadi, Editor(s)

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