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11 September 2003 Infrared buried mine detection performance prediction
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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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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