Land mine detection using fuzzy clustering in DARPA backgrounds: data collected with the Geo-Center ground-penetrating radar

Paul D. Gader; James M. Keller; Hongwu Liu

doi:10.1117/12.324165

4 September 1998 Land mine detection using fuzzy clustering in DARPA backgrounds: data collected with the Geo-Center ground-penetrating radar

Paul D. Gader, James M. Keller, Hongwu Liu

Author Affiliations +

Proceedings Volume 3392, Detection and Remediation Technologies for Mines and Minelike Targets III; (1998) https://doi.org/10.1117/12.324165
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

Fuzzy Clustering is applied to the problem of detecting landmines in Ground Penetrating Radar (GPR). The DARPA Backgrounds Data provides a rich source of signatures derived from a cluttered environment with a variety of sensors. One sensor used in the Backgrounds collection was the GPR developed and fielded by Geo-Centers, Inc. This GPR provides a three-dimensional array of intensity returns corresponding to a volume underneath the ground. In this paper, a novel approach to processing that GPR is described. The approach relies on computing edge direction and magnitude features in the volume and comparing them to prototypes generated using fuzzy c-means clustering. A confidence map is generated corresponding to the surface traversed by the system. The confidence map is thresholded to produce detections. Experimental results show a reduction in false alarm rates from about 40% using the standard processing method to about 4% using the three-dimensional, fuzzy clustering method.

Citation Download Citation

Paul D. Gader, James M. Keller, and Hongwu Liu "Land mine detection using fuzzy clustering in DARPA backgrounds: data collected with the Geo-Center ground-penetrating radar", Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); https://doi.org/10.1117/12.324165

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