In recent years, machine learning (ML) algorithms have become widespread in all the fields of remote sensing (RS) and earth observation (EO). This has allowed the rapid development of new procedures to solve problems affecting these sectors. In this context, this work aims at presenting a novel method for filtering speckle noise from Sentinel-1 ground range detected (GRD) data by applying deep learning (DL) algorithms, based on convolutional neural networks (CNNs). The paper provides an easy yet very effective approach to extract the large amount of training data needed for DL approaches in this challenging case. The experimental results on simulated speckled images and an actual SAR dataset show a clear improvement with respect to the state of the art in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), equivalent number of looks (ENL), proving the effectiveness of the proposed architecture.

翻译：近年来,在遥感(RS)和地球观测(EO)的所有领域,机器学习算法已经普及,从而能够迅速发展新的程序,解决影响这些部门的问题,在这方面,这项工作旨在提出一种新颖的方法,通过应用基于共生神经网络(CNNs)的深层次学习算法(DL),过滤从Sentinel-1测得的地面范围(Sentinel-1)数据中发现的闪烁噪声。本文提供了一种简单而非常有效的方法,以提取在这个富有挑战性的情况下DL方法所需的大量培训数据。模拟的浮斑图像和实际的SAR数据集的实验结果表明,在最高信号对噪音比率(PSNR)、结构相似指数(SSIM)等量的外观(ENL)方面,在证明拟议结构的有效性方面,在最新信号对噪音比率(PSRRRR)、结构相似指数(SSIM)方面,出现了明显的改进。