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Proceedings Paper

Techniques for improving buried mine detection in thermal IR imagery
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Paper Abstract

We describe sensor-based and signal-processing-based techniques for improving the detection of buried land mines in thermal IR imagery. Results of experimental studies using MWIR and LWIR imaging systems are reported. Thermal clutter due to surface reflected sunlight and skylight are investigated and shown to be the dominant clutter component for both MWIR and LWIR imagery collected during daylight hours. A sensor-based clutter reduction technique, spectral differencing, was considered and found to provide some benefit. The temporal evolution of thermal signatures was investigated. The imagery are found to have near-Gaussian statistics, and therefore the deflection coefficient is a valid measure of detectability. The deflection coefficient for some buried mines was found to improve with time after sunset. In addition, the LWIR band appears to offer some advantages in detection. Clutter mitigation via signal processing is also explored using an 'estimator-classifier' technique in which target-related parameters are estimated from the data and detected with a classifier. The theoretical basis of the method is discussed. MWIR and LWIR imagery are used to illustrate both the sensor-based and signal-processing-based techniques.

Paper Details

Date Published: 2 August 1999
PDF: 12 pages
Proc. SPIE 3710, Detection and Remediation Technologies for Mines and Minelike Targets IV, (2 August 1999); doi: 10.1117/12.357009
Show Author Affiliations
Ibrahim Kursat Sendur, The Ohio State Univ. (United States)
Brian A. Baertlein, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 3710:
Detection and Remediation Technologies for Mines and Minelike Targets IV
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

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