Fuzzy clustering is applied to the problem of detecting landmines. Sensor data generated by a Ground Penetrating Radar (GPR) is processed to detect the mines. The GPR produces a three dimensional array of intensity values, representing a volume below the surface of the ground. Features are computed from this array and clustered using a fuzzy competitive agglomerative (CA) algorithm. Prototypes are produced by the clustering algorithms and used to detect landmines. A novel aspect of this work is that the prototypes are not used in a nearest prototype style classifier, which would be the standard approach. Rather, the prototypes are used to provide a reliable indicator of the strength and pattern of a return at a location beneath the surface. Results on real, difficult data are provided that indicate that the fuzzy clustering produces more reliable detection outputs. In particular, the false alarm rates are much lower than those of the existing system.


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    Title :

    Fuzzy clustering for land mine detection


    Contributors:
    Frigui, H. (author) / Gader, P. (author) / Keller, J. (author)


    Publication date :

    1998


    Size :

    5 Seiten, 8 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English





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