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4 October 1999 Detection of small objects using adaptive wavelet-based template matching
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Wavelet-based detection algorithms are developed for detection small targets in non-Gaussian clutter. A wavelet transform is applied to reduce spatial correlation of the clutter. An adaptive matched filter is applied in the wavelet transform domain which uses estimated covariance matrices derived from the wavelet coefficients. Two problems hinder the use of covariance estimates for background clutter removal: slow computational speed and induced false alarms resulting from nearly singular covariance estimates due to small sample sizes. The issue of speed is dealt with by evaluating the covariance matrices on a sparse grid followed by low order interpolation. To control the problem of bad covariance estimates we filter the grid generated covariance matrices to remove outliers using peer group averaging. This procedure removes the false alarm problem associated with nearly singular covariance estimates without degrading the overall performance of the clutter removal process.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gary A. Hewer, Charles Kenny, Grant Hanson, Wei Kuo, and Lawrence A. Peterson "Detection of small objects using adaptive wavelet-based template matching", Proc. SPIE 3809, Signal and Data Processing of Small Targets 1999, (4 October 1999);

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