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Proceedings Paper

Land mine detection using fuzzy clustering in DARPA backgrounds: data collected with the Geo-Center ground-penetrating radar
Author(s): Paul D. Gader; James M. Keller; Hongwu Liu
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Paper 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.

Paper Details

Date Published: 4 September 1998
PDF: 11 pages
Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); doi: 10.1117/12.324165
Show Author Affiliations
Paul D. Gader, Univ. of Missouri/Columbia (United States)
James M. Keller, Univ. of Missouri/Columbia (United States)
Hongwu Liu, Univ. of Missouri/Columbia (United States)


Published in SPIE Proceedings Vol. 3392:
Detection and Remediation Technologies for Mines and Minelike Targets III
Abinash C. Dubey; James F. Harvey; J. Thomas Broach, Editor(s)

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