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Proceedings Paper

Algorithms for detection of surface mines in multispectral IR and visible imagery
Author(s): Wen-Jiao Liao; De-Hui Chen; Brian A. Baertlein
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Paper 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.

Paper Details

Date Published: 18 October 2001
PDF: 11 pages
Proc. SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, (18 October 2001); doi: 10.1117/12.445482
Show Author Affiliations
Wen-Jiao Liao, The Ohio State Univ. (United States)
De-Hui Chen, The Ohio State Univ. (United States)
Brian A. Baertlein, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 4394:
Detection and Remediation Technologies for Mines and Minelike Targets VI
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Vivian George, Editor(s)

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