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