Data fusion of computer-aided detection/computer-aided classification algorithms for classification of mines in very shallow water environments

Abstract

A method for combining the outputs of three different computer aided detection/computer aided classification (CAD/CAC) algorithms is presented and applied to a set of sidescan sonar data taken in the very shallow water environment, where the CAD/CAC algorithms are each tuned to detect mine-like objects. The fusion center receives from each algorithm the planar image coordinates and a confidence factor associated with individual CAD/CAC contacts, and produces fused classification reports of the mine-like objects. Since the three CAD/CAC algorithms use very different approaches, we make the reasonable assumption that valid classifications are nearby each other and false alarms occur randomly in the image. The resultant geometric clustering eliminates most of the false alarms while maintaining a high level of correct classification performance. Our unique fusion algorithm takes a constrained optimization approach, which minimizes the total number of false alarms over the clustering distance and cluster confidence factor thresholds for a given probability of correct classification. Resultant receiver operating characteristics show a significant reduction in the number of false contacts: the false alarm rate from any individual CAD/CAC algorithm is reduced by a factor of four or greater through the optimized data fusion processing.