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Proceedings Paper

Multiresolution detection of small objects using bootstrap methods and wavelets
Author(s): Gary A. Hewer; Wei Kuo; Lawrence A. Peterson
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Paper Abstract

A Daubechies' wavelet-based constant false alarm rate (CFAR) small-target detection algorithm is evaluated using measured and simulated infrared images. The wavelet-based detection algorithm is compared with the matched filter to establish relative performance curves. The adaptive CFAR detection statistics are derived from the lexicographically ordered image vectors using Efron's bootstrap method. The bootstrap employs repeated resampling to overcome the difficulties of modeling the post-transform detection statistics of the underlying clutter or fixed pattern noise. The performance of the detection algorithm is evaluated using a simulated Gaussian target with parametrically varying amplitude, size, and polarity. It is embedded in fixed pattern noise and measured images that will stress the detection algorithms.

Paper Details

Date Published: 31 May 1996
PDF: 12 pages
Proc. SPIE 2759, Signal and Data Processing of Small Targets 1996, (31 May 1996); doi: 10.1117/12.241159
Show Author Affiliations
Gary A. Hewer, 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. 2759:
Signal and Data Processing of Small Targets 1996
Oliver E. Drummond, Editor(s)

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