Combined detection of surface changes and deformation anomalies using amplitude-augmented recursive InSAR time series

Fengming Hu*, Freek J. van Leijen, Ling Chang, Jicang Wu, Ramon Hanssen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
30 Downloads (Pure)


Synthetic aperture radar (SAR) missions with short repeat times enable opportunities for near real-time deformation monitoring. Traditional multitemporal interferometric SAR (MT-InSAR) is able to monitor long-term and periodic deformation with high precision by time-series analysis. However, as time series lengthen, it is time-consuming to update the current results by reprocessing the whole dataset. Additionally, the number of coherent scatterers varies over time due to disappearing and emerging scatterers due to inevitable changes in surface scattering, and potential deformation anomalies require changes in the prevailing deformation model. Here, we propose a novel method to analyze InSAR time series recursively and detect both significant changes in scattering as well as deformation anomalies based on the new acquisitions. Sequential change detection is developed to identify temporary coherent scatterers (TCSs) using amplitude time series. Based on the predicted phase residuals, scatterers with abnormal deformation displacements are identified by a generalized ratio test, while the parameters of stable scatterers are updated using Kalman filtering. The quality of the anomaly detection is assessed based on the detectability power and the minimum detectable deformation. This facilitates (near) real-time data processing and decreases the false alarm likelihood. Experimental results show that the technique can be used for the real-time evaluation of deformation risks.
Original languageEnglish
Number of pages16
JournalIEEE transactions on geoscience and remote sensing
Publication statusPublished - 8 Jul 2021


  • Anomaly detection
  • Change detection
  • multitemporal InSAR
  • recursive process


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