Reconstructing Horizontal Displacement Through Deep Learning in Multiple-Pairwise Satellite Image Correlation

Data collected from optical remote sensing systems can be used to effectively measure ground horizontal displacements related for example to earthquake ruptures, glacier flows, dune migrations, and slow landslides. However, displacement of interest is often corrupted with and even hidden under a considerable amount of unwanted noise. Most traditional methods can eliminate such noise but may change unexpectedly our concerned displacement pattern. If the clean displacement can be recovered, with unwanted noise removed, we can more accurately interpret how tectonic process contributes to surface displacement. With the recently successful application in geodetic deformation denoising, deep learning is also expected to be able to address the problem associated with optical displacement. Here, we trained a deep-learning-based autoencoder on a real noise dataset generated from a large amount of Sentinel-2 images. We applied the autoencoder to synthetic and real test datasets to successfully eliminate the unwanted noises in given MPICs, accurately revealing noise-free displacement signals. Interestingly, we even discovered detailed surface migrating information about a dune near the Maduo earthquake in the Tibet Plateau. The finding highlights the autoencoder is a robust method that can also improve horizontal displacement caused by those tectonic processes other than just earthquakes.