<title>Application of fusion algorithims for computer-aided detection and classification of bottom mines to shallow-water test data</title>

Abstract

The fusion of multiple Computer Aided Detection/Computer Aided Classification (CAD/CAC) algorithms has been shown to be effective in reducing the false alarm rate associated with the automated classification of bottom mine-like objects when applied to side-scan sonar images taken in Very Shallow Water (VSW) environments. This paper reports on the application of such CAD/CAC Fusion algorithms to the shallow water environment, using sidescan sonar data taken in the Gulf of Mexico during April 2000. The fusion algorithm accepts the classification confidence levels and associated contact locations from two 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. Two different fusion criteria are evaluated: the first based on the Fisher Discriminant, and the second based on 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. The Fisher-based fusion provided an 82% probability of correct classification at a false alarm rate of 0.034 false alarms per image per side (port or starboard). This performance represented a 2:1 reduction in false alarms over a single CAD/CAC algorithm at this same probability of correct classification. The cluster confidence fusion algorithm performed nearly as well, yielding the 82% correct classification probability at a false alarm rate of 0.039 false alarms per image per side.