TADGAN-Based Anomaly Detection for PS-InSAR Deformation

Interferometric Synthetic Aperture Radar (InSAR) has become a widely used and efficient tool for monitoring large-scale, long-term land subsidence. Most applications, especially those related to risky assessment, utilize only the mean deformation rate derived through a linear fit of the InSAR-derived time series deformations without considering the full-time sequence. In this way, the embedded information, such as the onset, duration, and patterns of abnormal deformations, is ignored. This information, however, should not be compromised as it is critical for assessing the risky deformations and recognizing the causal factors. Due to the large data volume, processing the full-time-series information derived by InSAR analysis can be a challenge. In this study, we propose to use the Time-series Anomaly Detection Generative Adversarial Network (TadGAN) to deal with the time series InSAR deformation and recognize the onset and duration of abnormal epochs. The proposed method has been tested with InSAR-derived results over the Hong Kong airport region. It performs better than conventional risk assessment methods, such as the one based on Mean Absolute Deviation (MAD) outlier detection.