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Cloud and Shadow Removal for Sentinel-2 by Progressively Spatiotemporal Patch Group Learning

In this work, a progressively spatio-temporal patch group learning framework for cloud and shadow removal in Sentinel-2 data is proposed. Through sorting the spatial and corresponding multi-temporal patches with masks as the patch group fashion, a spatiotemporal patch group recovering model is developed using a global-local deep CNN. Finally, all the ergodic patches are weighted aggregated with integrity measure, then updated spatial data and its mask are regenerated through progressive iteration. Two experiments have been performed to demonstrate the effectiveness of the proposed method on Sentienl-2 MSI data, with single/multiple temporal imageries in small and largescale scenarios.