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Synthetic Aperture Radar (SAR) image understanding is crucial in remotesensing applications, but it is hindered by its intrinsic noise contamination,called speckle. Sophisticated statistical models, such as the $\mathcal{G}^0$family of distributions, have been employed to SAR data and many of the currentadvancements in processing this imagery have been accomplished throughextracting information from these models. In this paper, we proposeimprovements to parameter estimation in $\mathcal{G}^0$ distributions using theMethod of Log-Cumulants. First, using Bayesian modeling, we construct thatregularly produce reliable roughness estimates under both $\mathcal{G}^0_A$ and$\mathcal{G}^0_I$ models. Second, we make use of an approximation of theTrigamma function to compute the estimated roughness in constant time, makingit considerably faster than the existing method for this task. Finally, we showhow we can use this method to achieve fast and reliable SAR image understandingbased on roughness information.

Improving Log-Cumulant Based Estimation of Roughness Information in SAR imagery