A New Deep-Learning Approach for the Sub-Pixel Registration of Satellite Images Containing Sharp Displacement Discontinuities

Tristan Montagnon, James Hollingsworth, Erwan Pathier, Mathilde Marchandon, Mauro Dalla Mura, Sophie Giffard-Roisin

Abstract: Image correlation is a powerful method for remotely constraining ground displacements associated with natural disasters. By employing sub-pixel correlation algorithms, one can obtain a displacement field by correlating satellite images acquired before and after a displacement event. However, this computation may be biased when dealing with sharp discontinuities, typical of earthquake surface ruptures, which are of current interest in the context of quantifying the partitioning of slip between the primary fault core and neighboring damage zone. In this paper, we present an innovative deep learning method to perform sub-pixel correlation of optical satellite images for the retrieval of ground displacement, designed to mitigate bias around fault ruptures. From the generation of a realistic simulated database of images before and after synthetic ground displacement built specifically to deal with fault discontinuities in satellite images (e.g. Landsat-8 in this case), we developed a Convolutional Neural Network (CNN) able to retrieve sub-pixel displacements. Comparison with a state-of-the-art phase correlation method shows our pipeline is able to mitigate the sub-pixel bias in the near-field of earthquake ruptures.