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.

翻译：地球与环境多模态学习研讨会（MultiEarth 2023）旨在利用长期收集的海量遥感数据，对地球生态系统健康进行监测与分析。其中子任务——多模态SAR-to-EO图像翻译，要求在恶劣天气和光照条件下利用鲁棒的SAR数据，将其转化为高质量、清晰且视觉美观的EO数据。在SAR2EO任务中，EO数据中存在云层或遮挡物可能构成挑战。针对这一问题，我们提出清洁收集算法（CCA），旨在充分利用无云SAR数据并消除可能阻碍数据学习过程的因素。随后，我们应用pix2pixHD进行SAR到EO的翻译，并采用Restormer进行图像增强。在最终评估中，"CDRL"团队以0.07313的MAE成绩位居排行榜首位。