Uncertainty Quantification with Deep Ensemble Methods for Super-Resolution of Sentinel 2 Satellite Images
Author:
Affiliation:
1. CentraleSupélec, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
2. MF-DAS OP-EO Data Science, Deutsches Zentrum für Luft- und Raumfahrt, 82234 Weßling, Germany
Publisher
MDPI
Link
https://www.mdpi.com/2673-9984/9/1/4/pdf
Reference19 articles.
1. Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems;Mura;Int. J. Appl. Earth Obs. Geoinf.,2018
2. Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery;Castillo;ISPRS J. Photogramm. Remote Sens.,2017
3. Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, B., and Noges, T. (2016). First Experiences in Mapping LakeWater Quality Parameters with Sentinel-2 MSI Imagery. Remote Sens., 8.
4. Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network;Lanaras;ISPRS J. Photogramm. Remote Sens.,2018
5. A critical comparison among pansharpening algorithms;Vivone;IEEE Trans. Geosci. Remote Sens.,2014
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