Improving mine recognition through processing and Dempster-Shafer fusion of ground-penetrating radar data

A methodfor modeling andcombination of measures extractedfrom a ground-penetrating radar (GPR) in terms of belief functions within the Dempster-Shafer framework is presentedandillustratedon a real GPR data set. A starting point in the analysis is a preprocessed C-scan of a sand-lane containing some mines andfalse alarms. In order to improve the selection of regions of interest on such a preprocessed C-scan, a methodfor detecting suspectedareas is developed, basedon region analysis aroundthe local maxima. Once the regions are selected, a detailedanalysis of the chosen measures is performed for each of them. Two sets of measures are extracted and modeledin terms of belief functions. Finally, for every suspected region, masses assigned by each of the measures are combined, leading to a first guess on whether there is a mine or a non-dangerous object in the region. The region selection methodimproves detection, while the combination methodresults in significant improvements, especially in eliminating most of the false alarms.