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(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.
| Original language | English |
|---|---|
| Pages (from-to) | 1402-1407 |
| Number of pages | 6 |
| Journal | Oceans Conference Record |
| Volume | 3 |
| Publication status | Published - 2003 |
| Event | Celebrating the Past... Teaming Toward the Future - San Diego, CA., United States Duration: 22 Sept 2003 → 26 Sept 2003 |