Gaussian Markov Random Field Modeling of Textures in High-Frequency Synthetic Aperture Sonar Images

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.