Application of fusion algorithms for computer-aided detection and classification of bottom mines to shallow water test data from the battle space preparation autonomous underwater vehicle (BPAUV)

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 SW side-scan image data taken by the Battle Space Preparation Autonomous Underwater Vehicle (BPAUV), a vehicle made by Bluefin Robotics. 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 three other CAD/CAC processors is evaluated. The fusion algorithm accepts the classification confidence levels and associated contact locations from the four 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. Four different fusion criteria are evaluated: the first based on thresholding the sum of the confidence factors for the clustered contacts, the second and third based on simple and constrained binary combinations of the multiple CAD/CAC processor outputs, and the fourth based on the Fisher Discriminant. The resulting performance of the four fusion algorithms is compared, and the overall performance benefit of a significant reduction of false alarms at high correct classification probabilities is quantified. The optimal Fisher fusion algorithm yields a 90% probability of correct classification at a false alarm probability of 0.0062 false alarms per image per side, a 34:1 reduction in false alarms relative to the best performing single CAD/CAC algorithm.