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Proceedings Paper

Detection of small objects using adaptive wavelet-based template matching
Author(s): Gary A. Hewer; Charles Kenny; Grant Hanson; Wei Kuo; Lawrence A. Peterson
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Paper Abstract

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.

Paper Details

Date Published: 4 October 1999
PDF: 12 pages
Proc. SPIE 3809, Signal and Data Processing of Small Targets 1999, (4 October 1999); doi: 10.1117/12.364011
Show Author Affiliations
Gary A. Hewer, Naval Air Warfare Ctr. (United States)
Charles Kenny, Univ. of California/Santa Barbara (United States)
Grant Hanson, Naval Air Warfare Ctr. (United States)
Wei Kuo, Naval Air Warfare Ctr. (United States)
Lawrence A. Peterson, Naval Air Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 3809:
Signal and Data Processing of Small Targets 1999
Oliver E. Drummond, Editor(s)

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