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

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arxiv 2307.07123 v1 pith:TUZOPDXF submitted 2023-07-14 cs.CV eess.IV

Minseok Seo , Youngtack Oh , Doyi Kim , Dongmin Kang , Yeji Choi This is my paper · ORCID
classification cs.CVeess.IV
keywords floodframeworkdatadiffusion-basedimageimagesinsightsrapid
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation

    cs.CV 2024-11 conditional novelty 5.0 of 10

    Fine-tuning Stable Diffusion's U-Net with a confidence-weighted noise loss translates SAR to optical imagery with large FID and LPIPS gains over GAN and diffusion baselines on three datasets.

  2. PBVS 2024 Solution: Self-Supervised Learning and Sampling Strategies for SAR Classification in Extreme Long-Tail Distribution

    cs.CV 2024-12 reject novelty 4.0 of 10

    A PBVS 2024 solution report combining DINOv2, Lee filtering, SAR-to-EO translation, and undersampling achieves 9th place (21.45% accuracy) without ablations or code.

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