1st Place Solution to MultiEarth 2023 Challenge on Multimodal SAR-to-EO Image Translation
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Abstract (arXiv)
The Multimodal Learning for Earth and Environment Workshop (MultiEarth 2023) aims to harness the substantial amount of remote sensing data gathered over extensive periods for the monitoring and analysis of Earth's ecosystems'health. The subtask, Multimodal SAR-to-EO Image Translation, involves the use of robust SAR data, even under adverse weather and lighting conditions, transforming it into high-quality, clear, and visually appealing EO data. In the context of the SAR2EO task, the presence of clouds or obstructions in EO data can potentially pose a challenge. To address this issue, we propose the Clean Collector Algorithm (CCA), designed to take full advantage of this cloudless SAR data and eliminate factors that may hinder the data learning process. Subsequently, we applied pix2pixHD for the SAR-to-EO translation and Restormer for image enhancement. In the final evaluation, the team 'CDRL' achieved an MAE of 0.07313, securing the top rank on the leaderboard.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 22 Jun 2023.
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References · 9
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,627
- 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,179
- 2022BBDM: Image-to-image Translation with Brownian Bridge Diffusion Modelscited by 225
- 2022Unsupervised Change Detection Based on Image Reconstruction Losscited by 22
- 2022Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagerycited by 19
- 2022Dual Pyramid Generative Adversarial Networks for Semantic Image Synthesiscited by 7
- 2023MultiEarth 2023 -- Multimodal Learning for Earth and Environment Workshop and Challengecited by 6
- 2022SAR-to-EO Image Translation with Multi-Conditional Adversarial Networkscited by 3
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