An automated change detection approach for mine recognition using sidescan sonar data

  • Authors:

  • Shuang Wei;Henry Leung;Vincent Myers

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada;Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada;Defence R&D Canada, Dartmouth, Nova Scotia, Canada

  • Venue:
  • SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
  • Year:
  • 2009

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Abstract

This paper presents a new automated approach for the mine detection and classification (MDC) problem based on change detection techniques using sidescan sonar images. Adopting change detection techniques benefits this approach to recognize mine targets without training data or prior assumption required in traditional detection methods. In this approach, post-classification comparison is designed to detect the changes and the statistical information of pixel distribution is employed for change decision analysis. Specifically, because of the special characteristics of shadows in sonar images, shape and coarseness features are taken into account and play an important role in this method. This approach was successfully applied to two sets of bitemporal sidescan sonar images and the results are presented in this paper. The results prove the applicability of this approach for mine detection.