Infrared buried mine detection performance prediction

Herbert A. Duvoisin; Thomas R. Witten

doi:10.1117/12.486083

11 September 2003 Infrared buried mine detection performance prediction

Proceedings Volume 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII; (2003) https://doi.org/10.1117/12.486083
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

This paper answers in the affirmative the question: will it ever be feasible to predict useful infrared buried mine detection performance? The infrared (IR) is essentially blind at certain hours, but can have excellent vision at other times. The trick to making the IR a tactically useful tool is to plan mine detection operations during its best time of utility. Rather than use thermal models with their difficulty in representing IR imagery, we used a matched filter detector on IR video, in combination with prediction techniques using neural nets and weather data, to show that weather conditions can be successful in predicting IR mine detection performance. Prediction using mine detection models and weather data, correlated using neural nets and then predicted using weather data alone is not only theoretically feasible, but is also practical. Feasibility was demonstrated in Train A/Test A mode, where the neural nets achieved 100% prediction accuracy for both AP and AT mines. Practicality was demonstrated using single day Train A/Test B results, where 98% to 88% accuracy was achieved for AT mines from 2.5 to 12.5 hours forward, respectively. The technique is expected to be limited only by the accuracy of the short-term weather forecast.

Citation Download Citation

Herbert A. Duvoisin and Thomas R. Witten "Infrared buried mine detection performance prediction", Proc. SPIE 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII, (11 September 2003); https://doi.org/10.1117/12.486083

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KEYWORDS

Land mines

Mining

Infrared imaging

Data modeling

Thermography

Infrared cameras

Infrared detectors

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