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Open Access

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

EURASIP Journal on Advances in Signal Processing20092009:109438

Received: 31 July 2008

Accepted: 3 March 2009

Published: 6 April 2009


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.


Feature ExtractionClassification AccuracyQuantum InformationSonarGeometrical Feature

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Authors’ Affiliations

NATO Undersea Research Centre (NURC), La Spezia, Italy
ESRIN, European Space Agency (ESA), Frascati, Italy


© J. Del Rio Vera et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.