Scene Estimation from Speckled Synthetic Aperture Radar Imagery: Markov-Random-Field Approach

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Optical Society of America

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Journal of the Optical Society of America A

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A novel Markov-random-field model for speckled synthetic aperture radar (SAR) imagery is derived according to the physical, spatial statistical properties of speckle noise in coherent imaging. A convex Gibbs energy function for speckled images is derived and utilized to perform speckle-compensating image estimation. The image estimation is formed by computing the conditional expectation of the noisy image at each pixel given its neighbors, which is further expressed in terms of the derived Gibbs energy function. The efficacy of the proposed technique, in terms of reducing speckle noise while preserving spatial resolution, is studied by using both real and simulated SAR imagery. Using a number of commonly used metrics, the performance of the proposed technique is shown to surpass that of existing speckle-noise-filtering methods such as the Gamma MAP, the modified Lee, and the enhanced Frost.


Journal of the Optical Society of America A, Vol. 23, No. 6 (2006): 1269-1281. DOI.

M.M. Hayat was affiliated with the University of New Mexico, Albuquerque at the time of publication.