Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion

Optical remote sensing imagery is at the core of many Earth observation activities. The regular, consistent and global-scale nature of the satellite data is exploited in many applications, such as cropland monitoring, climate change assessment, land-cover and land-use classification, and disaster assessment. However, one main problem severely affects the temporal and spatial availability of surface observations, namely cloud cover. The task of removing clouds from optical images has been subject of studies since decades. The advent of the Big Data era in satellite remote sensing opens new possibilities for tackling the problem using powerful data-driven deep learning methods. In this paper, a deep residual…

• H
  Helmholtz-Gemeinschaft
• BF
  Bundesministerium für Bildung und Forschung
• BF
  Bundesministerium für Wirtschaft und Technologie
• ER
  European Research Council