Open Access

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

EURASIP Journal on Advances in Signal Processing20082008:876092

Received: 30 May 2007

Accepted: 11 January 2008

Published: 17 February 2008


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.

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Authors’ Affiliations

Institut TELECOM; TELECOM Bretagne, UEB; CNRS Lab-STICC/CID, Image and Information Processing Department
GIPSA-Lab, Signals and Images Department, Grenoble INP
Groupe d'Etudes Sous-Marines de l'Atlantique, DGA/DET/GESMA


© F. Maussang et al. 2008

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.