Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentation

Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images

Reducing speckle and limiting the variations of the physical parameters in Synthetic Aperture Radar (SAR) images is often a key-step to fully exploit the potential of such data. Nowadays, deep learning approaches produce state of the art results in single-image SAR restoration. Nevertheless, huge multi-temporal stacks are now often available and could be efficiently exploited to further improve image quality. This paper explores two fast strategies employing a single-image despeckling algorithm, namely SAR2SAR, in a multi-temporal framework. The first one is based on Quegan filter and replaces…

基于区域去噪扩散概率模型的SAR去斑

SAR Despeckling via Regional Denoising Diffusion Probabilistic Model

针对大规模SAR图像去斑难题，本文提出区域去噪扩散概率模型（R-DDPM），通过单次训练实现多尺度去斑，并利用区域引导逆向采样避免伪影，在Sentinel-1数据上表现优异。

deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling

Deep learning (DL) has proven to be a suitable approach for despeckling synthetic aperture radar (SAR) images. So far, most DL models are trained to reduce speckle that follows a particular distribution, either using simulated noise or a specific set of real SAR images, limiting the applicability of these methods for real SAR images with unknown noise statistics. In this paper, we present a DL method, deSpeckNet1, that estimates the speckle noise distribution and the despeckled image simultaneously. Since it does not depend on a specific noise model, deSpeckNet generalizes well across SAR acqu…

Despeckling Polarimetric SAR Data Using a Multi-Stream Complex-Valued Fully Convolutional Network

A Polarimetric Synthetic Aperture Radar (PolSAR) sensor is able to collect images in different polarization states, making it a rich source of information for target characterization. PolSAR images are inherently affected by speckle. Therefore, before deriving ad hoc products from the data, the polarimetric covariance matrix needs to be estimated by reducing speckle. In recent years, deep learning based despeckling methods have started to evolve from single channel SAR images to PolSAR images. To this aim, deep learning based approaches separate the real and imaginary components of the complex…

一种基于残差卷积神经网络的 Sentinel-1 SAR 地距检测数据斑点噪声滤波器

A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networks

本文提出一种基于残差 CNN 的 Sentinel-1 GRD SAR 斑点噪声滤波方法，并设计了训练数据提取方案，实验在 PSNR、SSIM 和 ENL 上优于现有方法。

基于深度学习的极化SAR图像斑点滤波方法及其在Sentinel-1上的应用

Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1

本研究提出了一种基于卷积神经网络的极化SAR图像斑点滤波完整框架，通过复协方差矩阵的可逆变换和变化检测策略，在有效抑制斑点的同时保持分辨率且不引入伪影，适用于进一步的极化处理。