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

Simon Y. Foo; James T. Cobb; Jason R. Stack

doi:10.1117/12.775539

29 April 2008 Gaussian Markov random field modeling of textures in high-frequency synthetic aperture sonar images

Simon Y. Foo, James T. Cobb, Jason R. Stack

Proceedings Volume 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII; 69530L (2008) https://doi.org/10.1117/12.775539
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

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.

Citation Download Citation

Simon Y. Foo, James T. Cobb, and Jason R. Stack "Gaussian Markov random field modeling of textures in high-frequency synthetic aperture sonar images", Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 69530L (29 April 2008); https://doi.org/10.1117/12.775539

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