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

Gargiulo Massimiliano, Mazza Antonio, Gaetano Raffaele, Ruello Giuseppe, Scarpa Giuseppe. 2018. A CNN-Based Fusion Method for Super-Resolution of Sentinel-2 Data. In : Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium: Observing, Understanding And Forecasting The Dynamics Of Our Planet. IEEE. Piscataway : IEEE, 4713-4716. ISBN 978-1-5386-7150-4 International Geoscience and Remote Sensing Symposium (IGARSS 2018), Valencia, Espagne, 22 Juillet 2018/27 Juillet 2018.

Résumé : 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).

Auteurs et affiliations

• Gargiulo Massimiliano, University of Naples Federico II (ITA)
• Mazza Antonio, University of Naples Federico II (ITA)
• Gaetano Raffaele, CIRAD-ES-UMR TETIS (FRA)
• Ruello Giuseppe, Université de Naples 2 (ITA)
• Scarpa Giuseppe, CIRAD-ES-UMR TETIS (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/592816/)

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