Title :
Automated segmentation of SAS images using the mean - standard deviation plane for the detection of underwater mines
Author :
Maussang, FrCx00E9;d?©ric ; Chanussot, Jocelyn ; H?©tet, Alain
Author_Institution :
Lab. des Images et des Signaux, Domaine Univ., St. Martin d´´Heres, France
Abstract :
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.
Keywords :
geophysical prospecting; geophysical signal processing; image segmentation; military systems; oceanographic techniques; sonar imaging; statistical analysis; synthetic aperture sonar; automated segmentation; entropy criterion; image segmentation; mean standard deviation plane; statistical analysis; synthetic aperture sonar images; underwater mines echoes; Amplitude estimation; Dissolved gas analysis; Equations; Image resolution; Image segmentation; Shape; Signal resolution; Speckle; Synthetic aperture sonar; Underwater tracking;
Conference_Titel :
OCEANS 2003. Proceedings
Print_ISBN :
0-933957-30-0
DOI :
10.1109/OCEANS.2003.178236
