Revealing Urban Deformation Patterns through InSAR Time Series Analysis with TCN and Transfer Learning

Abstract

Current multi-epoch InSAR techniques heavily rely on the assumption of linear deformation. This can sometimes overlook crucial deformation signals when using velocities for evaluation. The process of interpreting InSAR time series is not only time-consuming and labor-intensive but also requires a certain level of expertise. This study refines existing InSAR deformation categories, such as stable, linear, step, piecewise linear, power, and undefined, to define 'canonical deformation time series patterns.' We propose an innovative approach for InSAR post-processing using Temporal Convolutional Networks (TCN) and transfer learning. Due to the limited availability of real data, we use simulated data to train a pre-existing model. We then assess the effectiveness of our method in identifying urban deformation patterns. This research could significantly improve our understanding of large-scale InSAR time series deformation and reveal the underlying patterns.