23 May 2011 Seabed segmentation in synthetic aperture sonar images
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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.
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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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KEYWORDS
Image segmentation

Image processing algorithms and systems

Detection and tracking algorithms

Statistical modeling

Autoregressive models

Feature extraction

Image filtering

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