Sub-pixel optical satellite image registration for ground deformation using deep learning IEEE International Conference on Image Processing (IEEE ICIP) Montagnon, T. Hollingsworth, J. Pathier, E. Marchandon, M. Dalla Mura, M. /Giffard-Roisin, Sophie 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. IEEE 2022 text https://www.documentation.ird.fr/hor/fdi:010086243 fdi:010086243 Montagnon T., Hollingsworth J., Pathier E., Marchandon M., Dalla Mura M., Giffard-Roisin Sophie. Sub-pixel optical satellite image registration for ground deformation using deep learning. In : . IEEE International Conference on Image Processing (IEEE ICIP) IEEE, 2022, 2716-2720 IEEE International Conference on Image Processing, 29., Bordeaux (FRA), 2022/10/16-19 EN