# Improved Flood Insights: Diffusion-Based SAR to EO Image Translation

arXiv: https://arxiv.org/abs/2307.07123 · HTML: https://mlsanity.com/paper/2307.07123/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 14 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://mlsanity.com/paper/2205.07680/) (2022)
- [MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Network](https://mlsanity.com/paper/2304.01598/) (2023)
- [Unsupervised Change Detection Based on Image Reconstruction Loss](https://mlsanity.com/paper/2204.01200/) (2022)
- [SAR Despeckling using a Denoising Diffusion Probabilistic Model](https://mlsanity.com/paper/2206.04514/) (2022)
- [Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2212.10236/) (2022)
- [SAR2EO: A High-resolution Image Translation Framework with Denoising Enhancement](https://mlsanity.com/paper/2304.04760/) (2023)

## Built on by (most cited first, 5 of 5)

- [Diffusion Models in Low-Level Vision: A Survey](https://mlsanity.com/paper/2406.11138/) (2024)
- [Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives](https://mlsanity.com/paper/2404.08926/) (2024)
- [C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation](https://mlsanity.com/paper/2411.10788/) (2024)
- [PBVS 2024 Solution: Self-Supervised Learning and Sampling Strategies for SAR Classification in Extreme Long-Tail Distribution](https://mlsanity.com/paper/2412.12565/) (2024)
- [Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications](https://mlsanity.com/paper/2404.11243/) (2024)

## Abstract (arXiv)

Driven by rapid climate change, the frequency and intensity of flood events are increasing. Electro-Optical (EO) satellite imagery is commonly utilized for rapid response. However, its utilities in flood situations are hampered by issues such as cloud cover and limitations during nighttime, making accurate assessment of damage challenging. Several alternative flood detection techniques utilizing Synthetic Aperture Radar (SAR) data have been proposed. Despite the advantages of SAR over EO in the aforementioned situations, SAR presents a distinct drawback: human analysts often struggle with data interpretation. To tackle this issue, this paper introduces a novel framework, Diffusion-Based SAR to EO Image Translation (DSE). The DSE framework converts SAR images into EO images, thereby enhancing the interpretability of flood insights for humans. Experimental results on the Sen1Floods11 and SEN12-FLOOD datasets confirm that the DSE framework not only delivers enhanced visual information but also improves performance across all tested flood segmentation baselines.