A Deep Learning Approach for Change Points Detection in InSAR Time Series

Interferometric SAR (InSAR) algorithms exploit synthetic aperture radar (SAR) images to estimate ground displacements, which are updated at each new satellite acquisition, over wide areas. The analysis of the resulting time series finds its application, among others, in monitoring tasks regarding seismic faults, subsidence, landslides, and urban structures, for which an accurate and timely response is required. Typical analyses consist of identifying among the numerous time series the ones that exhibit an anomalous displacement, thus deserving to be further investigated. In practice, this is realized by selecting the time series that is characterized by trend changes w.r.t. the historical behavior. In this work, we propose a deep learning approach for change point detection in the InSAR time series. The designed architecture combines long short-term memory (LSTM) cells, to model the temporal correlation among samples in the input time series, and time-gated LSTM (TGLSTM) cells, to consider the sampling rate as additional information during learning. We further propose a solution to the lack of ground truth by developing a suitable pipeline for realistic data simulation. The method has been developed and validated through a large suite of experiments. Both quantitative and qualitative analyses have been conducted to demonstrate the detection capabilities of the learned model and how it is a valid alternative to the statistical reference algorithm. We further applied the developed method in a real continuous monitoring project to analyze the InSAR time series over the Tuscany region in Italy, proving its effectiveness in the real domain.