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Thanks to their temporal-spatial coverage and free access, Sentinel-2 imagesare very interesting for the community. However, a relatively coarse spatialresolution, compared to that of state-of-the-art commercial products, motivatesthe study of super-resolution techniques to mitigate such a limitation.Specifically, thirtheen bands are sensed simultaneously but at differentspatial resolutions: 10, 20, and 60 meters depending on the spectral location.Here, building upon our previous convolutional neural network (CNN) basedmethod, we propose an improved CNN solution to super-resolve the 20-mresolution bands benefiting spatial details conveyed by the accompanying 10-mspectral bands.

Advances on CNN-based super-resolution of Sentinel-2 images