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4 September 1998 Land mine detection using fuzzy clustering in DARPA backgrounds: data collected with the Geo-Center ground-penetrating radar
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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