Abstract
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 (MLOs) are first detected using a Markov random field (MRF) model. The highlight and shadow regions of these MLOs 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 monoimage and multiimage classification. Results for all phases are shown on real data.
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