Automatic target recognition in synthetic aperture sonar images based on geometrical feature extraction

Authors:
J. Del Rio Vera;E. Coiras;J. Groen;B. Evans
Affiliations:
NATO Undersea Research Centre, La Spezia, Italy and ESRIN, European Space Agency, Frascati, Italy;NATO Undersea Research Centre, La Spezia, Italy;NATO Undersea Research Centre, La Spezia, Italy;NATO Undersea Research Centre, La Spezia, Italy
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2009

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Abstract

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.