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Proceedings Paper

Gaussian Markov random field modeling of textures in high-frequency synthetic aperture sonar images
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Paper Abstract

This paper describes our attempts to model sea bottom textures in high-frequency synthetic aperture sonar imagery using a Gaussian Markov random field. A least-squares estimation technique is first used to estimate the model parameters of the down-sampled grey-scale sonar images. To qualitatively measure estimation results, a fast sampling algorithm is then used to synthesize the sea bottom textures of a fourth-order Gaussian Markov random field which is then compared with the original sonar image. A total of four types of sea floor texture are used in the case study. Results show that the 4th order GMRF model mimics patchy sandy textures and sand ripple, but does not reproduce more complex textures exhibited by coral and rock formations.

Paper Details

Date Published: 29 April 2008
PDF: 8 pages
Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 69530L (29 April 2008); doi: 10.1117/12.775539
Show Author Affiliations
Simon Y. Foo, Florida State Univ. (United States)
James T. Cobb, Naval Surface Warfare Ctr. (United States)
Jason R. Stack, Naval Surface Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 6953:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII
Russell S. Harmon; John H. Holloway; J. Thomas Broach, Editor(s)

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