Combining electromagnetic induction and automated classification in a UXO discrimination blind test

Juan Pablo Fernández; Benjamin Barrowes; Alex Bijamov; Tomasz Grzegorczyk; Kevin A. O'Neill; Irma Shamatava; Fridon Shubitidze

doi:10.1117/12.850446

29 April 2010 Combining electromagnetic induction and automated classification in a UXO discrimination blind test

Juan Pablo Fernández, Benjamin Barrowes, Alex Bijamov, Tomasz Grzegorczyk, Kevin A. O'Neill, Irma Shamatava, Fridon Shubitidze

Author Affiliations +

Proceedings Volume 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV; 766403 (2010) https://doi.org/10.1117/12.850446
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

The Strategic Environmental Research and Development Program (SERDP) is administering benchmark blind tests of increasing realism to the UXO community. One of the latest took place at Aberdeen Proving Ground in Maryland: 214 cells, each one containing at most one buried target, were interrogated with the TEMTADS electromagnetic induction (EMI) sensor array. Each item could be one of six standard ordnance or could be harmless clutter such as shrapnel. The test called for singling out potentially dangerous items and classifying them. Our group divided the task into three steps: location, characterization, and classification. For the first step the HAP method was used. The method assumes a pure dipolar response from the target and finds the position and orientation using the measured field and its associated scalar potential, the latter computed using a layer of equivalent sources. For target characterization we used the NSMS model, which employs an ensemble of dipole sources arranged on a spheroidal surface. The strengths of these sources are normalized by the primary field that strikes them; their surface integral is an electromagnetic signature that can be used as a classifier. In this work we look into automating the classification step using a multi-category support vector machine (SVM). The algorithm runs binary SVMs for every combination of pairs of target candidates, apportions votes to the winners, and assigns unknown examples to the category with the most votes. We look for the feature combinations and SVM parameters that result in the most expedient and accurate classification.

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Juan Pablo Fernández, Benjamin Barrowes, Alex Bijamov, Tomasz Grzegorczyk, Kevin A. O'Neill, Irma Shamatava, and Fridon Shubitidze "Combining electromagnetic induction and automated classification in a UXO discrimination blind test", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 766403 (29 April 2010); https://doi.org/10.1117/12.850446

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