Application of fusion algorithms for computer aided detection and classification of bottom mines to synthetic aperture sonar test data

Abstract

Over the past several years, Raytheon Company has adapted its Computer Aided Detection/Computer-Aided Classification (CAD/CAC) algorithm to process side-scan sonar imagery taken in both the Very Shallow Water (VSW) and Shallow Water (SW) operating environments. This paper describes the further adaptation of this CAD/CAC algorithm to process Synthetic Aperture Sonar (SAS) image data taken by an Autonomous Underwater Vehicle (AUV). The tuning of the CAD/CAC algorithm for the vehicle's sonar is described, the resulting classifier performance is presented, and the fusion of the classifier outputs with those of another CAD/CAC processor is evaluated. The fusion algorithm accepts the classification confidence levels and associated contact locations from the different CAD/CAC algorithms, clusters the contacts based on the distance between their locations, and then declares a valid target when a clustered contact passes a prescribed fusion criterion. Three different fusion criteria are evaluated: the first based on thresholding the sum of the confidence factors for the clustered contacts, the second based on simple binary combinations of the multiple CAD/CAC processor outputs, and the third based on the Fisher Discriminant. The resulting performance of the three fusion algorithms is compared, and the overall performance benefit of a significant reduction of false alarms at high correct classification probabilities is quantified.