Skip to main content
  • Research Article
  • Open access
  • Published:

Fusion of Local Statistical Parameters for Buried Underwater Mine Detection in Sonar Imaging


Detection of buried underwater objects, and especially mines, is a current crucial strategic task. Images provided by sonar systems allowing to penetrate in the sea floor, such as the synthetic aperture sonars (SASs), are of great interest for the detection and classification of such objects. However, the signal-to-noise ratio is fairly low and advanced information processing is required for a correct and reliable detection of the echoes generated by the objects. The detection method proposed in this paper is based on a data-fusion architecture using the belief theory. The input data of this architecture are local statistical characteristics extracted from SAS data corresponding to the first-, second-, third-, and fourth-order statistical properties of the sonar images, respectively. The interest of these parameters is derived from a statistical model of the sonar data. Numerical criteria are also proposed to estimate the detection performances and to validate the method.

Publisher note

To access the full article, please see PDF.

Author information

Authors and Affiliations


Corresponding author

Correspondence to F. Maussang.

Rights and permissions

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

Reprints and permissions

About this article

Cite this article

Maussang, F., Rombaut, M., Chanussot, J. et al. Fusion of Local Statistical Parameters for Buried Underwater Mine Detection in Sonar Imaging. EURASIP J. Adv. Signal Process. 2008, 876092 (2008).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: