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Proceedings Paper

Inferring the location of buried UXO using a support vector machine
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Paper Abstract

The identification of unexploded ordnance (UXO) using electromagnetic-induction (EMI) sensors involves two essentially independent steps: Each anomaly detected by the sensor has to be located fairly accurately, and its orientation determined, before one can try to find size/shape/composition properties that identify the object uniquely. The dependence on the latter parameters is linear, and can be solved for efficiently using for example the Normalized Surface Magnetic Charge model. The location and orientation, on the other hand, have a nonlinear effect on the measurable scattered field, making their determination much more time-consuming and thus hampering the ability to carry out discrimination in real time. In particular, it is difficult to resolve for depth when one has measurements taken at only one instrument elevation. In view of the difficulties posed by direct inversion, we propose using a Support Vector Machine (SVM) to infer the location and orientation of buried UXO. SVMs are a method of supervised machine learning: the user can train a computer program by feeding it features of representative examples, and the machine, in turn, can generalize this information by finding underlying patterns and using them to classify or regress unseen instances. In this work we train an SVM using measured-field information, for both synthetic and experimental data, and evaluate its ability to predict the location of different buried objects to reasonable accuracy. We explore various combinations of input data and learning parameters in search of an optimal predictive configuration.

Paper Details

Date Published: 26 April 2007
PDF: 9 pages
Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 65530B (26 April 2007);
Show Author Affiliations
Juan Pablo Fernández, Dartmouth College (United States)
Keli Sun, Dartmouth College (United States)
Benjamin Barrowes, U.S. Army Corps of Engineers (United States)
Kevin O'Neill, Dartmouth College (United States)
U.S. Army Corps of Engineers (United States)
Irma Shamatava, Dartmouth College (United States)
Fridon Shubitidze, Dartmouth College (United States)
Keith D. Paulsen, Dartmouth College (United States)

Published in SPIE Proceedings Vol. 6553:
Detection and Remediation Technologies for Mines and Minelike Targets XII
Russell S. Harmon; J. Thomas Broach; John H. Holloway Jr., Editor(s)

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