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31 May 1996 Multiresolution detection of small objects using bootstrap methods and wavelets
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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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gary A. Hewer, Wei Kuo, and Lawrence A. Peterson "Multiresolution detection of small objects using bootstrap methods and wavelets", Proc. SPIE 2759, Signal and Data Processing of Small Targets 1996, (31 May 1996);

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