Optical image gap filling using deep convolutional autoencoder from optical and radar images

Cresson Rémi, Ienco Dino, Gaetano Raffaele, Osé Kenji, Tong Minh Dinh Ho. 2019. Optical image gap filling using deep convolutional autoencoder from optical and radar images. In : IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium proceedings. IEEE, GRSS. Yokohama : IEEE, 218-221. ISBN 978-1-5386-9155-7 IGARSS 2019, Yokohama, Japon, 28 Juillet 2019/2 Août 2019.

Résumé : A major issue affecting optical imagery is the presence of clouds. The need of cloud-free scenes at specific date is crucial in a number of operational monitoring applications. On the other hand, the cloud-insensitive SAR sensors are a solid asset and they provide orthogonal information with respect to optical satellite, that enable the retrieval of information lost in optical images due to cloud cover. In the context of an increasing availability of both optical and SAR images, thank to the Sentinel constellation, we propose a deep learning method to reconstruct (gap-fill) optical data, polluted by cloud phenomena, exploiting multi-temporal SAR and optical images.

Auteurs et affiliations

• Cresson Rémi, IRSTEA (FRA)
• Ienco Dino, IRSTEA (FRA)
• Gaetano Raffaele, CIRAD-ES-UMR TETIS (FRA)
• Osé Kenji, IRSTEA (FRA)
• Tong Minh Dinh Ho, IRSTEA (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/605043/)

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