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.

本文介绍了一个旨在通过利用广泛时间内收集的遥感数据，对地球生态健康进行监测和分析的跨模态学习多个任务的工作坊，并提出了清洁收集器算法，以清除可能妨碍数据学习过程的因素，并对图像进行增强，最终在SAR-to-EO任务中实现了较好的表现。

Multimodal SAR到EO图像转换的MultiEarth 2023挑战中的第一名解决方案