18 October 2001 Algorithms for detection of surface mines in multispectral IR and visible imagery
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Abstract
Algorithms are presented for detecting surface mines using multi-spectral data. The algorithms are demonstrated using visible and MWIR imagery collected at Fort A.P. Hill, VA under a variety of conditions. For imagery with a resolution of a few centimeters there is significant correlation in the clutter. Using a first-order Gauss Markov random field model for the clutter, an efficient pre-whitening filter is proposed. A significant improvement in detection is demonstrated as a result of this whitening. Further improvement in the detection of specific mine types is demonstrated by using a random signal model with a known covariance matrix. That approach leads to an estimator-correlator formulation, in which the random signature estimate is the output of a Wiener filter. It is suggested that by fusing the output of a bank of such filters one could improve detection of all mine types.
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Wen-Jiao Liao, Wen-Jiao Liao, De-Hui Chen, De-Hui Chen, Brian A. Baertlein, Brian A. Baertlein, } "Algorithms for detection of surface mines in multispectral IR and visible imagery", Proc. SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, (18 October 2001); doi: 10.1117/12.445482; https://doi.org/10.1117/12.445482
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