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Proceedings Paper

Enhanced buried UXO detection via GPR/EMI data fusion
Author(s): Matthew P. Masarik; Joseph Burns; Brian T. Thelen; Jack Kelly; Timothy C. Havens
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Paper Abstract

This paper investigates the enhancements to detection of buried unexploded ordinances achieved by combining ground penetrating radar (GPR) data with electromagnetic induction (EMI) data. Novel features from both the GPR and the EMI sensors are concatenated as a long feature vector, on which a non-parametric classifier is then trained. The classifier is a boosting classifier based on tree classifiers, which allows for disparate feature values. The fusion algorithm was applied to a government-provided dataset from an outdoor testing site, and significant performance enhancements were obtained relative to classifiers trained solely on the GPR or EMI data. It is shown that the performance enhancements come from a combination of improvements in detection and in clutter rejection.

Paper Details

Date Published: 3 May 2016
PDF: 9 pages
Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98230R (3 May 2016); doi: 10.1117/12.2223009
Show Author Affiliations
Matthew P. Masarik, Michigan Technological Univ. (United States)
Joseph Burns, Michigan Technological Univ. (United States)
Brian T. Thelen, Michigan Technological Univ. (United States)
Jack Kelly, Michigan Technological Univ. (United States)
Timothy C. Havens, Michigan Technological Univ. (United States)

Published in SPIE Proceedings Vol. 9823:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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