14 May 2015 Approach to explosive hazard detection using sensor fusion and multiple kernel learning with downward-looking GPR and EMI sensor data
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Proceedings Volume 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX; 94540B (2015); doi: 10.1117/12.2176856
Event: SPIE Defense + Security, 2015, Baltimore, Maryland, United States
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.
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Anthony Pinar, Matthew Masarik, Timothy C. Havens, Joseph Burns, Brian Thelen, John Becker, "Approach to explosive hazard detection using sensor fusion and multiple kernel learning with downward-looking GPR and EMI sensor data", Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 94540B (14 May 2015); doi: 10.1117/12.2176856; https://doi.org/10.1117/12.2176856
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KEYWORDS
Sensors

Electromagnetic coupling

General packet radio service

Explosives

Sensor fusion

Explosives detection

Image fusion

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