Synthetic Aperture Radar (SAR) image understanding is crucial in remote sensing 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 current advancements in processing this imagery have been accomplished through extracting information from these models. In this paper, we propose improvements to parameter estimation in $\mathcal{G}^0$ distributions using the Method of Log-Cumulants. First, using Bayesian modeling, we construct that regularly produce reliable roughness estimates under both $\mathcal{G}^0_A$ and $\mathcal{G}^0_I$ models. Second, we make use of an approximation of the Trigamma function to compute the estimated roughness in constant time, making it considerably faster than the existing method for this task. Finally, we show how we can use this method to achieve fast and reliable SAR image understanding based on roughness information.

翻译：合成孔径雷达（SAR）图像理解在遥感应用中至关重要，但其固有噪声污染（称为散斑）阻碍了该过程。复杂的统计模型，如$\mathcal{G}^0$分布族，已被应用于SAR数据，而当前该图像处理领域的许多进展都是通过从这些模型中提取信息实现的。本文提出利用对数累积量法改进$\mathcal{G}^0$分布的参数估计。首先，我们通过贝叶斯建模，构建能够在$\mathcal{G}^0_A$和$\mathcal{G}^0_I$模型下稳定产生可靠粗糙度估计的方法。其次，我们利用Trigamma函数的近似表达式，以恒定时间计算估计的粗糙度，显著快于此任务的现有方法。最后，我们展示了如何利用该方法基于粗糙度信息实现快速可靠的SAR图像理解。