Improvements in computer-aided detection/computer-aided classification (CAD/CAC) of bottom mines through post analysis of a diverse set of very shallow water (VSW) environmental test data

Abstract

In 1999 Raytheon adapted its shallow-water Side-Looking Sonar (SLS) Computer Aided Detection/Computer Aided Classification (CAD/CAC) algorithm to process side-scan sonar data obtained with the Woods Hole Oceanographic Institute's Remote Environmental Monitoring Units (REMUS) autonomous underwater vehicle (AUV). To date, Raytheon has demonstrated the ability to effectively execute mine-hunting missions with the REMUS vehicle through the fusion of its CAD/CAC algorithm with several other CAD/CAC algorithms to achieve low false alarm rates while maintaining a high probability of correct detection/classification. Mine-hunting in the very shallow water (VSW) environment poses a host of difficulties including such issues as: a higher incidence of man made clutter, significant interference due to biological sources (such as kelp or silt), the scouring of mines into the bottom, interference from surface/bottom bounce, and image distortion due to vehicle motion during image generation. These issues coupled with highly variable bottom conditions and small bottom targets make reliable hunting in the VSW environment very difficult. In order to be operationally viable, the individual CAD/CAC algorithms must demonstrate robustness over these very different mine-hunting environments. A higher normalized false alarm rate per algorithm is considered acceptable based on the false alarm reduction achieved through multi-algorithm fusion. Raytheon's recent CAD/CAC algorithm enhancements demonstrate a significant improvement in overall CAD/CAC performance across a diverse set of environments, from the relatively benign Gulf of Mexico environment to the more challenging areas off the coast of southern California containing significant biological and bottom clutter. The improvements are attributed to incorporating an image normalizer into the algorithm's pre-processing stage in conjunction with several other modifications. The algorithm enhancements resulted in an 11% increase in overall correct classification probability with an accompanying 17% reduction in false alarm rate, when averaged over the multiple environments. The paper discusses the algorithm enhancements and presents the detailed performance results.