Fusion of Local Statistical Parameters for Buried Underwater Mine Detection in Sonar ImagingReportar como inadecuado

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1 TAMCIC - Traitement Algorithmique et Matériel de la Communication, de l-Information et de la Connaissance 2 GIPSA-GPIG - GPIG GIPSA-DIS - Département Images et Signal 3 GIPSA-SIGMAPHY - SIGMAPHY GIPSA-DIS - Département Images et Signal 4 DGA-DET-GESMA - Groupe d-Etudes Sous-Marines de l-Atlantique

Abstract : 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.

Keywords : Data fusion Sonar Image Mines detection Belief theory

Autor: Frédéric Maussang - Michèle Rombaut - Jocelyn Chanussot - Alain Hétet - Maud Amate -

Fuente: https://hal.archives-ouvertes.fr/


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