Automated approach to classification of mine-like objects in sidescan sonar using highlight and shadow information

The majority of existing automatic mine detection algorithms which have been developed are robust at detecting mine-like objects (MLOs) at the expense of detecting many false alarms. These objects must later be classified as mine or not-mine. The authors present a model based technique using Dempster-Shafer information theory to extend the standard mine/not-mine classification procedure to provide both shape and size information on the object. A sonar simulator is used to produce synthetic realisations of mine-like object shadow regions which are compared to those of the unknown object using the Hausdorff distance. This measurement is fused with other available information from the object´s shadow and highlight regions to produce a membership function for each of the considered object classes. Dempster-Shafer information theory is used to classify the objects using both mono-view and multiview analysis. In both cases, results are presented on real data.