Automated Segmentation of SAS Images using the Mean - Standard Deviation Representation

A segmentation method of synthetic aperture sonar (SAS) images is presented, in order to highlight some characteristics (number, position, shape, ...) of underwater mines echoes. This segmentation method is based on statistical characteristics of the sonar images, highlighted by the mean – standard deviation plane. It is automated by using an entropy criterion.

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Additional Info

Field	Value
Source	MTS/IEEE Oceans'03 conference
Author	Maussang, Frederic, Chanussot, Jocelyn, Hétet, Alain
Maintainer	CCSD
Last Updated	May 9, 2026, 13:14 (UTC)
Created	May 9, 2026, 13:14 (UTC)
Identifier	hal-00086795
Language	en
contributor	Laboratoire des images et des signaux (LIS) ; Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
coverage	San Diego, United States
creator	Maussang, Frederic
date	2003-05-09T00:00:00
harvest_object_id	e8a1d276-cc34-4f47-acee-d16c76e11f20
harvest_source_id	3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title	test moissonnage SELUNE
metadata_modified	2025-09-27T00:00:00
set_spec	type:COMM