1st Place Solution to MultiEarth 2023 Challenge on Multimodal SAR-to-EO Image Translation
This work provides a solution for remote sensing applications by improving image translation under adverse conditions, though it is incremental as it builds on existing methods.
The paper tackled the problem of translating SAR data to EO imagery by addressing cloud obstructions in EO data, achieving a top leaderboard rank with an MAE of 0.07313.
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.