The analysis of Synthetic Aperture Radar (SAR) imagery is an important step in remote sensing applications, and it is a challenging problem due to its inherent speckle noise. One typical solution is to model the data using the $G_I^0$ distribution and extract its roughness information, which in turn can be used in posterior imaging tasks, such as segmentation, classification and interpretation. This leads to the need of quick and reliable estimation of the roughness parameter from SAR data, especially with high resolution images. Unfortunately, traditional parameter estimation procedures are slow and prone to estimation failures. In this work, we proposed a neural network-based estimation framework that first learns how to predict underlying parameters of $G_I^0$ samples and then can be used to estimate the roughness of unseen data. We show that this approach leads to an estimator that is quicker, yields less estimation error and is less prone to failures than the traditional estimation procedures for this problem, even when we use a simple network. More importantly, we show that this same methodology can be generalized to handle image inputs and, even if trained on purely synthetic data for a few seconds, is able to perform real time pixel-wise roughness estimation for high resolution real SAR imagery.

翻译：合成孔径雷达(SAR)图像分析是遥感应用中的重要步骤，但其固有的散斑噪声使其成为极具挑战性的问题。一种典型解决方案是利用$G_I^0$分布对数据进行建模并提取其粗糙度信息，进而将粗糙度参数用于后续成像任务（如分割、分类与解译）。这就需要从SAR数据（尤其是高分辨率图像）中快速可靠地估计粗糙度参数。然而，传统参数估计方法速度缓慢且易出现估计失败。本文提出基于神经网络的估计框架：首先学习预测$G_I^0$样本的潜在参数，继而将其用于估计未知数据的粗糙度。实验结果表明，即使采用简单网络结构，该方法相比传统估计过程速度更快、估计误差更小且失败率更低。更重要的是，我们证明该方法可推广至图像输入场景：即便仅用纯合成数据训练数秒，也能对真实高分辨率SAR图像实现实时的逐像素粗糙度估计。