Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Fusion of Local Statistical Parameters for Buried Underwater Mine Detection in Sonar Imaging | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 876092 19 pages doi 2008 876092 Research Article Fusion of Local Statistical Parameters for Buried Underwater Mine Detection in Sonar Imaging F. Maussang 1 M. Rombaut 2 J. Chanussot 2 A. Hetet 3 and M. Amate3 1 Institut TELECOM TELECOM Bretagne UEB CNRS Lab-STICC CID Image and Information Processing Department Technopole Brest-Iroise - CS 83818 29238 Brest Cedex 3 France 2 GIPSA-Lab Signals and Images Department Grenoble INP INPG - 46 Avenue Felix Viallet 38031 Grenoble Cedex France 3 Groupe d Etudes Sous-Marines de l Atlantique DGA DET GESMA BP 42 29240 Brest Armées France Correspondence should be addressed to F. Maussang Received 30 May 2007 Revised 6 December 2007 Accepted 11 January 2008 Recommended by Ati Baskurt 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. Copyright 2008 F. Maussang et al. This is an open access article distributed under the Creative Commons Attribution License which permits .
