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

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.

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Correspondence to J. Del Rio Vera.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Del Rio Vera, J., Coiras, E., Groen, J. et al. Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction. EURASIP J. Adv. Signal Process. 2009, 109438 (2009). https://doi.org/10.1155/2009/109438

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  • DOI: https://doi.org/10.1155/2009/109438

Keywords

  • Feature Extraction
  • Classification Accuracy
  • Quantum Information
  • Sonar
  • Geometrical Feature