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- Title
MARRYING DEEP LEARNING AND DATA FUSION FOR ACCURATE SEMANTIC LABELING OF SENTINEL-2 IMAGES.
- Authors
Fonteix, G.; Swaine, M.; Leras, M.; Tarabalka, Y.; Tripodi, S.; Trastour, F.; Giraud, A.; Laurore, L.; Hyland, J.
- Abstract
The understanding of the Earth through global land monitoring from satellite images paves the way towards many applications including flight simulations, urban management and telecommunications. The twin satellites from the Sentinel-2 mission developed by the European Space Agency (ESA) provide 13 spectral bands with a high observation frequency worldwide. In this paper, we present a novel multi-temporal approach for land-cover classification of Sentinel-2 images whereby a time-series of images is classified using fully convolutional network U-Net models and then coupled by a developed probabilistic algorithm. The proposed pipeline further includes an automatic quality control and correction step whereby an external source can be introduced in order to validate and correct the deep learning classification. The final step consists of adjusting the combined predictions to the cloud-free mosaic built from Sentinel-2 L2A images in order for the classification to more closely match the reference mosaic image.
- Subjects
EUROPEAN Space Agency; DEEP learning; MULTISENSOR data fusion; TELECOMMUNICATION management; REMOTE-sensing images; QUALITY control; AUTOMATIC control systems
- Publication
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2021, Issue 3, p101
- ISSN
2194-9042
- Publication type
Article
- DOI
10.5194/isprs-annals-V-3-2021-101-2021