28 March 1995 Clutter reduction and target detection enhancement using wavelet transform techniques
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Abstract
In this paper, a multichannel optical wavelet processor and a matching pursuit processor capable of enhancing the detection of cluttered targets are presented. Wavelet functions have zero-mean and are virtually band-pass filters. In many cases, targets and clutter are separable in the spatial spectral domain. Therefore, by selecting wavelet functions that represent features of targets but are insensitive to that of clutter, targets can be extracted from the input scene while clutter is suppressed. Due to dyadic sampling, a multichannel optical wavelet processor with a limited number of channels can detect regions of interest for different targets. With matching pursuit decomposition, features of targets are extracted and represented in a few wavelets known as coherent structure; whereas clutter and noise are diluted across the dictionary. Clutter and noise can then be effectively removed from the signal by a simple thresholding operation. A time-frequency energy distribution can be derived from matching pursuit decomposition, which contains no interference terms and thus clearly characterized the input signal in the time-frequency plane. Optical architectures of these processors are described. Simulated and experimental results are provided.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiangyang Yang, Don A. Gregory, Peter S. Erbach, "Clutter reduction and target detection enhancement using wavelet transform techniques", Proc. SPIE 2490, Optical Pattern Recognition VI, (28 March 1995); doi: 10.1117/12.205767; https://doi.org/10.1117/12.205767
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