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Proceedings Paper

Seabed segmentation in synthetic aperture sonar images
Author(s): J. Tory Cobb; Jose Principe
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Paper Abstract

A synthetic aperture sonar (SAS) image segmentation algorithm using features from a parameterized intensity image autocorrelation function (ACF) is presented. A modification over previous parameterized ACF models that better characterizes periodic or rippled seabed textures is presented and discussed. An unsupervised multiclass k-means segmentation algorithm is proposed and tested against a set of labeled SAS images. Segmentation results using the various models are compared against sand, rock, and rippled seabed environments.

Paper Details

Date Published: 23 May 2011
PDF: 11 pages
Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 80170M (23 May 2011); doi: 10.1117/12.883048
Show Author Affiliations
J. Tory Cobb, Naval Surface Warfare Ctr. Panama City Div. (United States)
Jose Principe, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 8017:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI
Russell S. Harmon; John H. Holloway; J. Thomas Broach, Editor(s)

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