Inferring the location of buried UXO using a support vector machine

Juan Pablo Fernández; Keli Sun; Benjamin Barrowes; Kevin O'Neill; Irma Shamatava; Fridon Shubitidze; Keith D. Paulsen

doi:10.1117/12.718712

26 April 2007 Inferring the location of buried UXO using a support vector machine

Juan Pablo Fernández, Keli Sun, Benjamin Barrowes, Kevin O'Neill, Irma Shamatava, Fridon Shubitidze, Keith D. Paulsen

Proceedings Volume 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII; 65530B (2007) https://doi.org/10.1117/12.718712
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

Citation Download Citation

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, Detection and Remediation Technologies for Mines and Minelike Targets XII, 65530B (26 April 2007); https://doi.org/10.1117/12.718712

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