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 (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.

[1]  T. Aridgides,et al.  Fusion of adaptive algorithms for the classification of sea mines using high resolution side scan sonar in very shallow water , 2001, MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295).

[2]  Christophe Chesnaud,et al.  Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Scott Reed,et al.  An automatic approach to the detection and extraction of mine features in sidescan sonar , 2003 .

[4]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Scott Reed,et al.  Model-based approach to the detection and classification of mines in sidescan sonar. , 2004, Applied optics.

[6]  Judith M. Bell.  A model for the simulation of sidescan sonar , 1995 .

[7]  Gerald J. Dobeck,et al.  Automated detection and classification of sea mines in sonar imagery , 1997, Defense, Security, and Sensing.

[8]  Yvan Petillot,et al.  Automated approach to classification of mine-like objects in sidescan sonar using highlight and shadow information , 2004 .

[9]  C. M. Ciany,et al.  Computer aided detection/computer aided classification and data fusion algorithms for automated detection and classification of underwater mines , 2000, OCEANS 2000 MTS/IEEE Conference and Exhibition. Conference Proceedings (Cat. No.00CH37158).

[10]  Patrick Pérez,et al.  Three-Class Markovian Segmentation of High-Resolution Sonar Images , 1999, Comput. Vis. Image Underst..

[11]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..