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

arXiv: https://arxiv.org/abs/2102.00692 · HTML: https://mlsanity.com/paper/2102.00692/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [SAR2SAR: a semi-supervised despeckling algorithm for SAR images](https://mlsanity.com/paper/2006.15037/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images](https://mlsanity.com/paper/2207.11111/) (2022)

## Abstract (arXiv)

This paper presents a despeckling method for Sentinel-1 GRD images based on the recently proposed framework "SAR2SAR": a self-supervised training strategy. Training the deep neural network on collections of Sentinel 1 GRD images leads to a despeckling algorithm that is robust to space-variant spatial correlations of speckle. Despeckled images improve the detection of structures like narrow rivers. We apply a detector based on exogenous information and a linear features detector and show that rivers are better segmented when the processing chain is applied to images pre-processed by our despeckling neural network.