Multi-Sensor Time Series Cloud Removal Fusing Optical and SAR Satellite Information.

On average, about half of all optical satellite data observing Earth is covered by haze or clouds. These atmospheric disturbances hinder the ongoing observation of our planet and prevent the seamless application of established remote sensing methods. Accordingly, to allow for an ongoing monitoring of Earth, approaches to reconstruct optical space-borne observations are required. This work introduces a new data set, SEN12MS-CR-TS, for the purpose of multi-sensor time series cloud removal. SEN12MS-CR-TS consists of co-registered radar and optical satellite data, featuring a sequence of bi-weekly observations throughout an entire year. Finally, we demonstrate the usability of our novel data set by developing a new multi-sensor time-series cloud removal architecture. We are positive that our curated data set as well as the proposed model will advance future research in satellite image reconstruction and benefit the expanding adaptation of global and all-weather remote sensing applications.