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24 August 1999 HRR ATR using VQ classification with a reject option
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Batuhan Ulug and Stanley C. Ahalt "HRR ATR using VQ classification with a reject option", Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999);

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