Montagnon T., Hollingsworth J., Pathier E., Marchandon M., Dalla Mura M., Giffard-Roisin Sophie. (2022). Sub-pixel optical satellite image registration for ground deformation using deep learning. In :
IEEE International Conference on Image Processing (IEEE ICIP). Piscataway : IEEE, 2716-2720. IEEE International Conference on Image Processing, 29., Bordeaux (FRA), 2022/10/16-19. ISBN 978-1-6654-9621-6.
Titre du document
Sub-pixel optical satellite image registration for ground deformation using deep learning
Année de publication
2022
Type de document
Partie d'ouvrage
Auteurs
Montagnon T., Hollingsworth J., Pathier E., Marchandon M., Dalla Mura M., Giffard-Roisin Sophie
In
IEEE International Conference on Image Processing (IEEE ICIP)
Source
Piscataway : IEEE, 2022,
2716-2720 ISBN 978-1-6654-9621-6
Colloque
IEEE International Conference on Image Processing, 29., Bordeaux (FRA), 2022/10/16-19
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.
