Interpretable clustering of PS-InSAR time series for ground deformation detection

Abstract

Persistent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) provides high-precision ground deformation measurements over wide areas. However, analyzing PS time series remains challenging due to complex temporal patterns and the need to consider comprehensive displacement fields to fully characterize ground deformation processes. This study evaluates and compares unsupervised clustering approaches for PS time series analysis, contrasting feature extraction techniques against raw time series methods. We developed an online optimization algorithm for cluster number determination and introduced a custom density-based score (MLRD) for evaluating clustering quality in sparse geospatial datasets. The approaches were tested on Sentinel-1-derived PS data from the landslide-prone Offida municipality (Marche region, Italy), where feature-based methodologies demonstrated superior performance, achieving improvements of one to two orders of magnitude in clustering quality metrics compared to conventional approaches. The multivariate analysis notably outperformed univariate methods, with optimal MLRD ($2.59\cdot 10^{-5}$) and Calinski–Harabasz scores (194.73) at 50% explained variance, while preserving the physical interpretability of the results. This comprehensive analysis identified coherent deformation clusters extending beyond previously mapped landslide boundaries, demonstrating the effectiveness of multivariate clustering in detecting potentially unstable areas. This methodological framework advances PS time series analysis through robust pattern recognition while enhancing geohazard assessment capabilities, offering a robust foundation for identifying unstable areas and providing quantitative support for improving our understanding of complex ground deformation mechanisms.