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Inferring the location of buried UXO using a support vector machine
Proc. SPIE 6553, 65530B (2007); http://dx.doi.org/10.1117/12.718712
Monday 9 April 2007
Orlando, FL, USA
Detection and Remediation Technologies for Mines and Minelike Targets XII
Russell S. Harmon, J. Thomas Broach, John H. Holloway, Jr.
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Add to FacebookThe 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.
© 2007 COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Online Apr 26, 2007
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Juan Pablo Fernández, Keli Sun, Benjamin Barrowes, Kevin O'Neill, Irma Shamatava, Fridon Shubitidze and Keith D. Paulsen, "Inferring the location of buried UXO using a support vector machine",
Proc. SPIE 6553, 65530B (2007); http://dx.doi.org/10.1117/12.718712
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