# Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning

arXiv: https://arxiv.org/abs/2102.00682 · HTML: https://mlsanity.com/paper/2102.00682/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [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)
- [Learning a Dilated Residual Network for SAR Image Despeckling](https://mlsanity.com/paper/1709.02898/) (2017)
- [SAR2SAR: a semi-supervised despeckling algorithm for SAR images](https://mlsanity.com/paper/2006.15037/) (2020)
- [Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks](https://mlsanity.com/paper/2001.05264/) (2020)

## Built on by (most cited first, 3 of 3)

- [Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?](https://mlsanity.com/paper/2204.11493/) (2022)
- [Multi-temporal speckle reduction with self-supervised deep neural networks](https://mlsanity.com/paper/2207.11095/) (2022)
- [Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images](https://mlsanity.com/paper/2207.11111/) (2022)

## Abstract (arXiv)

Deep learning approaches show unprecedented results for speckle reduction in SAR amplitude images. The wide availability of multi-temporal stacks of SAR images can improve even further the quality of denoising. In this paper, we propose a flexible yet efficient way to integrate temporal information into a deep neural network for speckle suppression. Archives provide access to long time-series of SAR images, from which multi-temporal averages can be computed with virtually no remaining speckle fluctuations. The proposed method combines this multi-temporal average and the image at a given date in the form of a ratio image and uses a state-of-the-art neural network to remove the speckle in this ratio image. This simple strategy is shown to offer a noticeable improvement compared to filtering the original image without knowledge of the multi-temporal average.