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Proceedings Paper

Approach to explosive hazard detection using sensor fusion and multiple kernel learning with downward-looking GPR and EMI sensor data
Author(s): Anthony Pinar; Matthew Masarik; Timothy C. Havens; Joseph Burns; Brian Thelen; John Becker
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Paper Abstract

This paper explores the effectiveness of an anomaly detection algorithm for downward-looking ground penetrating radar (GPR) and electromagnetic inductance (EMI) data. Threat detection with GPR is challenged by high responses to non-target/clutter objects, leading to a large number of false alarms (FAs), and since the responses of target and clutter signatures are so similar, classifier design is not trivial. We suggest a method based on a Run Packing (RP) algorithm to fuse GPR and EMI data into a composite confidence map to improve detection as measured by the area-under-ROC (NAUC) metric. We examine the value of a multiple kernel learning (MKL) support vector machine (SVM) classifier using image features such as histogram of oriented gradients (HOG), local binary patterns (LBP), and local statistics. Experimental results on government furnished data show that use of our proposed fusion and classification methods improves the NAUC when compared with the results from individual sensors and a single kernel SVM classifier.

Paper Details

Date Published: 14 May 2015
PDF: 20 pages
Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 94540B (14 May 2015); doi: 10.1117/12.2176856
Show Author Affiliations
Anthony Pinar, Michigan Technological Univ. (United States)
Matthew Masarik, Michigan Technological Univ. (United States)
Timothy C. Havens, Michigan Technological Univ. (United States)
Joseph Burns, Michigan Technological Univ. (United States)
Brian Thelen, Michigan Technological Univ. (United States)
John Becker, Michigan Technological Univ. (United States)


Published in SPIE Proceedings Vol. 9454:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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