Clean Collector Algorithm for Satellite Image Pre-Processing of SAR-to-EO Translation

Abstract

In applications such as environmental monitoring, algorithms and deep learning-based methods using synthetic aperture radar (SAR) and electro-optical (EO) data have been proposed with promising results. These results have been achieved using already cleaned datasets for training data. However, in real-world data collection, data are often collected regardless of environmental noises (clouds, night, missing data, etc.). Without cleaning the data with these noises, the trained model has a critical problem of poor performance. To address these issues, we propose the Clean Collector Algorithm (CCA). First, we use a pixel-based approach to clean the QA60 mask and outliers. Secondly, we remove missing data and night-time data that can act as noise in the training process. Finally, we use a feature-based refinement method to clean the cloud images using FID. We demonstrate its effectiveness by winning first place in the SAR-to-EO translation track of the MultiEarth 2023 challenge. We also highlight the performance and robustness of the CCA on other cloud datasets, SEN12MS-CR-TS and Scotland&India.