ML-based characterization of PS-InSAR multi-mission point clouds for ground deformation classification

Persistent Scatterer Interferometry (PSI) is a powerful multitemporal A-DInSAR (Advanced Differential Synthetic Aperture Radar Interferometry) technique widely used for monitoring and measuring Earth’s surface displacements over large areas with sub-centimetric precision. The capability to detect ground deformation processes relies on the available PSI spatial density, strictly related to the resolution of the considered sensor and the presence of stable natural and artificial reflectors. A new data fusion approach, developed as part of the “MUSAR” project funded by ASI (Italian Space Agency), integrates multi-band SAR sensors to improve data coverage of PSI data by synthesizing multi-sensor displacement information. The integration of multi-mission PSI generates synthetic measurement points, named Ground Deformation Markers (GD-Markers), featuring vertical (Up-Down) and horizontal (Est-West) components of the displacements. The fusion of PSI data extracted by C-band Sentinel-1 images from the Copernicus initiative and the COSMO-SkyMed constellation in the X-band from ASI contributed to creating a dataset with high information content.

Each GD-Markers cluster with displacement measurements identifies a specific deformation process in the region of interest. After selecting the relevant cluster of points, the deformation processes were classified into different categories (e.g., landslide, subsidence) to improve their understanding and evaluation for mitigating natural-related hazards. This study aimed to develop a machine learning-based classification system, starting from GD-Markers point clouds, which support the automatization of ground displacement identification and characterization. The synthetic points were characterized as individual entities or point clouds, formed by a discrete cluster of points in space, to evaluate the advantage of treating each point independently or incorporating local neighborhood information. The structured point data were analyzed using a supervised Random Forest (RF) approach to evaluate the performance of point cloud classification and categorization for identifying the best initial setting. Each point was assigned a label representing a deformation process in point cloud classification, while one label is provided for the entire point cloud dataset with categorization.

Comparing models’ performances allowed the definition of the best possible approach for classifying the deformation processes observed by GD-Markers point clouds. The analysis assessed the effectiveness of the classification of single points or clusters to identify the optimal setup that achieves an accurate segmentation between adjacent deformation processes. Identifying this initial setting was essential for selecting and developing advanced deep-learning approaches.