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

arXiv: https://arxiv.org/abs/2104.09350 · HTML: https://mlsanity.com/paper/2104.09350/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 80 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack](https://mlsanity.com/paper/1811.09310/) (2018)
- [SAR image despeckling through convolutional neural networks](https://mlsanity.com/paper/1704.00275/) (2017)
- [Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks](https://mlsanity.com/paper/2007.02075/) (2020)
- [SAR Image Despeckling Using a Convolutional Neural Network](https://mlsanity.com/paper/1706.00552/) (2017)
- [SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy](https://mlsanity.com/paper/2006.15559/) (2020)
- [Generating High Quality Visible Images from SAR Images Using CNNs](https://mlsanity.com/paper/1802.10036/) (2018)
- [Automatic Dataset Builder for Machine Learning Applications to Satellite Imagery](https://mlsanity.com/paper/2008.01578/) (2020)
- [Unassisted Quantitative Evaluation Of Despeckling Filters](https://mlsanity.com/paper/1704.05952/) (2017)
- [Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning](https://mlsanity.com/paper/2011.11005/) (2020)

## Built on by (most cited first, 4 of 4)

- [A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification](https://mlsanity.com/paper/2205.12459/) (2022)
- [Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1](https://mlsanity.com/paper/2408.15678/) (2024)
- [SEN12-WATER: A New Dataset for Hydrological Applications and its Benchmarking](https://mlsanity.com/paper/2409.17087/) (2024)
- [Deep Learning for Remote Sensing to Improve Flood Inundation Mapping](https://mlsanity.com/paper/2606.02310/) (2026)

## Abstract (arXiv)

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.