Wavelet-based learning vector quantization for automatic target recognition

Lipchen Alex Chan; Nasser M. Nasrabadi; Vincent Mirelli

doi:10.1117/12.243151

14 June 1996 Wavelet-based learning vector quantization for automatic target recognition

Lipchen Alex Chan, Nasser M. Nasrabadi, Vincent Mirelli

Proceedings Volume 2755, Signal Processing, Sensor Fusion, and Target Recognition V; (1996) https://doi.org/10.1117/12.243151
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

An automatic target recognition classifier is constructed that uses a set of dedicated vector quantizers (VQs). The background pixels in each input image are properly clipped out by a set of aspect windows. The extracted target area for each aspect window is then enlarged to a fixed size, after which a wavelet decomposition splits the enlarged extraction into several subbands. A dedicated VQ codebook is generated for each subband of a particular target class at a specific range of aspects. Thus, each codebook consists of a set of feature templates that are iteratively adapted to represent a particular subband of a given target class at a specific range of aspects. These templates are then further trained by a modified learning vector quantization (LVQ) algorithm that enhances their discriminatory characteristics. A recognition rate of 69.0 percent is achieved on a highly cluttered test set.

Citation Download Citation

Lipchen Alex Chan, Nasser M. Nasrabadi, and Vincent Mirelli "Wavelet-based learning vector quantization for automatic target recognition", Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); https://doi.org/10.1117/12.243151

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