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A CNN-Based Fusion Method for Super-Resolution of Sentinel-2 Data

Gargiulo M., Mazza A., Gaetano R., Ruello G., Scarpa G.. 2018. In : Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium: Observing, Understanding And Forecasting The Dynamics Of Our Planet. Piscataway : IEEE, p. 4713-4716. International Geoscience and Remote Sensing Symposium (IGARSS 2018), 2018-07-22/2018-07-27, Valencia (Espagne).

DOI: 10.1109/IGARSS.2018.8518447

Sentinel-2 data represent a rich source of information for the community due to the free access and to the temporal-spatial coverage assured. However, some of the spectral bands are sensed at reduced resolution due to a compromise between technological limitations and Copernicus program's objectives. For this reason in this work we present a new super-resolution method based on Convolutional Neural Networks (CNNs) to rise the resolution of the short wave infra-red (SWIR) band from 20 to 10 meters, that is the highest resolution provided. This is accomplished by fusing the target band with the finer-resolution ones. The proposed solution compares favourably against several alternative methods according to different quality indexes. In addition we have also tested the use of the super-resolved band from an applicative perspective by detecting water basins through the Modified Normalized Difference Water Index (MNDWI).

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