Seabed segmentation in synthetic aperture sonar images

J. Tory Cobb; Jose Principe

doi:10.1117/12.883048

23 May 2011 Seabed segmentation in synthetic aperture sonar images

J. Tory Cobb; Jose Principe;

Author Affiliations +

Proceedings Volume 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI; 80170M (2011) https://doi.org/10.1117/12.883048
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

J. Tory Cobb, Jose Principe, "Seabed segmentation in synthetic aperture sonar images", Proc. SPIE 8017, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI, 80170M (23 May 2011); doi: 10.1117/12.883048; https://doi.org/10.1117/12.883048

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