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Proceedings Paper

Using physical models to improve thermal IR detection of buried mines
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Paper Abstract

Many aspects of a buried mine's thermal IR signature can be predicted through physical models, and insight provided by such models can lead to better detection. Several techniques for exploiting this information are described. The first approach involves ML estimation of model parameters and followed by classification of those parameters. We show that this approach is related to an approximate evaluation of an integral over the parameters that arises in a Bayesian formulation. This technique is compared with a generalized likelihood ratio test (GLRT) and with computationally efficient, model-free approaches, in which soil temperature data are classified directly. The benefit of using the temporal information is also investigated. Algorithm performance is illustrated using broadband IR imagery of buried mines acquired over a 24 hour period. It is found that the detection performance at a suitably selected time is comparable to the performance achieved by processing all times. The performance of the GLRT, for which detection is based only on the residual error, is inferior to a classifier using the parameters.

Paper Details

Date Published: 18 October 2001
PDF: 12 pages
Proc. SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, (18 October 2001); doi: 10.1117/12.445472
Show Author Affiliations
De-Hui Chen, The Ohio State Univ. (United States)
Ibrahim Kursat Sendur, The Ohio State Univ. (United States)
Wen-Jiao Liao, 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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