Advances on CNN-based super-resolution of Sentinel-2 images
This work addresses the limitation of coarse resolution in freely accessible Sentinel-2 images for remote sensing applications, but it is incremental as it builds on a previous CNN method.
The paper tackles the problem of enhancing the spatial resolution of Sentinel-2 satellite images, specifically super-resolving 20-meter resolution bands using details from 10-meter bands, and reports improved results with a CNN-based method.
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.