Montagnon T. auteur aut IRD Hollingsworth J. auteur aut IRD Pathier E. auteur aut IRD Marchandon M. auteur aut IRD Dalla Mura M. auteur aut IRD Giffard-Roisin Sophie auteur aut IRD text bookSection eng text/pdf reformatted digital access Precise estimation of ground displacement maps at regional scales from optical satellite imaging is fundamental for the comprehension of natural disasters, such as earthquakes. Current methods make use of correlation techniques between two acquisitions in order to retrieve a fractional pixel shift. Yet, differences in local lighting conditions between the two acquisitions can lead to high image differences which will bias the estimation of the displacement, and data-driven methods could have the ability to overcome these errors. From the generation of a realistic simulated database based on Landsat-8 satellite pairs of images with added simulated shifts, we developed a Convolutional Neural Network (CNN) able to retrieve a sub-pixel displacement. specialized 126TELAPP02 122TRAI 064GEOMOR 2716-2720 Piscataway IEEE 2022 IEEE International Conference on Image Processing, 29., Bordeaux (FRA), 2022/10/16-19 https://www.documentation.ird.fr/hor/fdi:010086243 10.1109/ICIP46576.2022.9897214 978-1-6654-9621-6 [F B010086243] https://www.documentation.ird.fr/hor/fdi:010086243 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2022-11/010086243.pdf IRD - Base Horizon / Pleins textes 2022-10-07 2025-02-24 fdi:010086243 fre