Automated approach to classification of mine-like objects in sidescan sonar using highlight and shadow information

The majority of existing automatic mine detection algorithms which have been developed are robust at detecting mine-like objects (MLOs) at the expense of detecting many false alarms. These objects must later be classified as mine or not-mine. The authors present a model based technique using Dempster–Shafer information theory to extend the standard mine/not-mine classification procedure to provide both shape and size information on the object. A sonar simulator is used to produce synthetic realisations of mine-like object shadow regions which are compared to those of the unknown object using the Hausdorff distance. This measurement is fused with other available information from the object's shadow and highlight regions to produce a membership function for each of the considered object classes. Dempster–Shafer information theory is used to classify the objects using both mono-view and multi-view analysis. In both cases, results are presented on real data.

[1]  Anil K. Jain,et al.  Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images , 1994, IEEE Trans. Geosci. Remote. Sens..

[2]  Isabelle Bloch,et al.  Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account , 1996, Pattern Recognit. Lett..

[3]  Mandyam D. Srinath,et al.  Contour sequence moments for the classification of closed planar shapes , 1987, Pattern Recognit..

[4]  JEFFREY WOOD,et al.  Invariant pattern recognition: A review , 1996, Pattern Recognit..

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

[6]  D.M. Lane,et al.  Superellipse fitting for the classification of mine-like shapes in side-scan sonar images , 2002, OCEANS '02 MTS/IEEE.

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

[8]  Dennis M. Buede,et al.  A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[9]  Yvan Petillot,et al.  Unsupervised mine detection and analysis in side-scan sonar: A Comparison of Markov Random Fields and Statistical Snakes , 2001 .

[10]  Paul Suetens,et al.  Road extraction from multi-temporal satellite images by an evidential reasoning approach , 1991, Pattern Recognit. Lett..

[11]  Isabelle Bloch,et al.  Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing , 1997, IEEE Trans. Geosci. Remote. Sens..

[12]  Isabelle Bloch.  Information combination operators for data fusion: a comparative review with classification , 1996, IEEE Trans. Syst. Man Cybern. Part A.

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

[14]  Patrick Pérez,et al.  Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification of Shadow Shapes in Sonar Imagery , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Emanuele Trucco,et al.  General Purpose Matching of Grey Level Arbitrary Images , 2001, IWVF.

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

[17]  Yvan Petillot,et al.  Unsupervised Segmentation of Object Shadow and Highlight using Statistical Snakes , 2001 .

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

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

[20]  I. Quidu,et al.  Mine classification using a hybrid set of descriptors , 2000, OCEANS 2000 MTS/IEEE Conference and Exhibition. Conference Proceedings (Cat. No.00CH37158).

[21]  Gerald J. Dobeck,et al.  Adaptive three-dimensional range-crossrange-frequency filter processing string for sea mine classification in side scan sonar imagery , 1997, Defense, Security, and Sensing.

[22]  Jan Flusser,et al.  Pattern recognition by affine moment invariants , 1993, Pattern Recognit..

[23]  Gerald J. Dobeck.  Algorithm fusion for automated sea mine detection and classification , 2001, MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295).

[24]  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).

[25]  Richard J. Prokop,et al.  A survey of moment-based techniques for unoccluded object representation and recognition , 1992, CVGIP Graph. Model. Image Process..

[26]  I. Quidu,et al.  Mine classification based on raw sonar data: an approach combining Fourier descriptors, statistical models and genetic algorithms , 2000, OCEANS 2000 MTS/IEEE Conference and Exhibition. Conference Proceedings (Cat. No.00CH37158).

[27]  Edward Tunstel,et al.  Safe navigation on hazardous terrain , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).