Approach to explosive hazard detection using sensor fusion and multiple kernel learning with downward-looking GPR and EMI sensor data
Document Type
Conference Proceeding
Publication Date
5-14-2015
Department
Department of Electrical and Computer Engineering; Center for Data Sciences
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.
Publication Title
Proceedings of SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets
Recommended Citation
Pinar, A.,
Masarik, M. P.,
Havens, T. C.,
Burns, J. W.,
Thelen, B.,
&
Becker, J.
(2015).
Approach to explosive hazard detection using sensor fusion and multiple kernel learning with downward-looking GPR and EMI sensor data.
Proceedings of SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets,
94540B.
http://doi.org/10.1117/12.2176856
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1052