Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction

This paper presents a new supervised classification approach for automated target recognition (ATR) in SAS images. The recognition procedure starts with a novel segmentation stage based on the Hilbert transform. A number of geometrical features are then extracted and used to classify observed objects against a previously compiled database of target and non-target features. The proposed approach has been tested on a set of 1528 simulated images created by the NURC SIGMAS sonar model, achieving up to 95% classification accuracy.

[1]  John C. Curlander,et al.  Synthetic Aperture Radar: Systems and Signal Processing , 1991 .

[2]  Philippe Blondel,et al.  Handbook of seafloor sonar imagery , 1997 .

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

[4]  Graham R. Brookes,et al.  Visualizing Underwater Environments Using Multifrequency Sonar , 1999, IEEE Computer Graphics and Applications.

[5]  E. Coiras,et al.  Simulation and 3D Reconstruction of Side-looking Sonar Images , 2009 .

[6]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[7]  W. Brown.  Synthetic Aperture Radar , 1967, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Michael P. Hayes,et al.  Broad-band synthetic aperture sonar , 1992 .

[9]  Anthony G. Constantinides,et al.  Image edge detection and segmentation based on the Hilbert transform , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[10]  Yvan Petillot,et al.  Adaptive fusion framework based on augmented reality training , 2008 .

[11]  Saman S. Abeysekera,et al.  An underwater target classification scheme based on the acoustic backscatter form function , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[12]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .

[13]  Michael J. Procopio,et al.  Using Binary Classifiers to Augment Stereo Vision for Enhanced Autonomous Robot Navigation ; CU-CS-1027-07 , 2007 .

[14]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[15]  A. Rosenfeld.  Introduction to machine vision , 1985, IEEE Control Systems Magazine.

[16]  Svetha Venkatesh,et al.  On the classification of image features , 1990, Pattern Recognit. Lett..