Post-Processing of InSAR Deformation Time Series Using Clustering-Based Pattern Identification

Abstract

Multi-temporal synthetic aperture radar interferometry (MT-InSAR) is a standard technique for mapping clustering and wide-scale deformation. A linear model is often used in phase unwrapping to overcome the underdetermination. It’s difficult to identify different types of nonlinear deformation. However, the interpretation of nonlinear deformation is very important in monitoring potential risk. This paper introduces a comprehensive approach for identifying and interpreting different types of deformation within InSAR datasets, integrating initial clustering and classification simplification. Initial classification is performed using the K-means clustering method to cluster the collected InSAR deformation time-series data. Then we use F test and Anderson-Darling test (AD test) to simplify the clusters after initial classification. This technique distinctly discerns the changing trends of deformation signals, thereby providing robust support for interpreting potential deformation scenarios within observed InSAR regions.

Keywords

multi-temporal synthetic aperture radar interferometry (MT-InSAR) / machine learning / hypothesis test / nonlinear deformation