A model based approach to mine detection and classification in sidescan sonar

Developments in Autonomous Underwater Vehicle(AUV) technology has shifted the direction of Mine-Counter-measure(MCM) research towards more automated techniques. This paper presents an automated approach to the detection and classification of mine-like objects using Sidescan Sonar images. Mine-like objects(MLO) are first detected using a Markov Random Field(MRF) model. The highlight and shadow regions of these MLO's are then extracted using a Co-operating Statistical Snake model. Objects which are not identified as false alarms are then considered in a third classification phase. A sonar simulator model considers different possible object shapes, measuring the plausibility of each match. A final classification decision is carried out using Dempster-Shafer theory which allows both mono-image and multi-image classification. Results for all phases are shown on real data.