Spatio-Temporal Quality Indicators for Differential Interferometric Synthetic Aperture Radar Data

Abstract

Satellite-based interferometric synthetic aperture radar (InSAR) is an invaluable technique in the detection and monitoring of changes on the surface of the earth. Its high spatial coverage, weather friendly and remote nature are among the advantages of the tool. The multi-temporal differential InSAR (DInSAR) methods in particular estimate the spatio-temporal evolution of deformation by incorporating information from multiple SAR images. Moreover, opportunities from the DInSAR techniques are accompanied by challenges that affect the final outputs. Resolving the inherent ambiguities of interferometric phases, especially in areas with a high spatio-temporal deformation gradient, represents the main challenge. This brings the necessity of quality indices as important DInSAR data processing tools in achieving ultimate processing outcomes. Often such indices are not provided with the deformation products. In this work, we propose four scores associated with (i) measurement points, (ii) dates of time series, (iii) interferograms and (iv) images involved in the processing. These scores are derived from a redundant set of interferograms and are calculated based on the consistency of the unwrapped interferometric phases in the frame of a least-squares adjustment. The scores reflect the occurrence of phase unwrapping errors and represent valuable input for the analysis and exploitation of the DInSAR results. The proposed tools were tested on 432,311 points, 1795 interferograms and 263 Sentinel-1 single look complex images by employing the small baseline technique in the PSI processing chain, PSIG of the geomatics division of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC). The results illustrate the importance of the scores—mainly in the interpretation of the DInSAR outputs.

A Multi-Temporal Small Baseline Interferometry Procedure Applied to Mining-Induced Deformation Monitoring

“…Future developments of this work will aim at assessing the performance of the adopted method in terms of the existing ones [17,19]. Quality indicators for the obtained products, e.g., [44], can be employed to provide further validation and to optimally tune the main algorithm parameters, i.e., minimum average coherence, minimum image redundancy, and the threshold for the equivalent spatial coherence used for the point selection. The study of the displacement time series has revealed the presence of abrupt changes due to the seismic activity that are extremely dangerous for the built environment and infrastructures.…”

Analysis of Mining-Induced Terrain Deformation Using Multitemporal Distributed Scatterer Sar Interferometry

“…The results were validated against in-situ GNSS measurements. Future developments will include implementing an interferogram filtering stage (Pepe et al, 2015) before the interferogam selection stage, as well as analysing the quality of the produced results using proper indicators (Wassie et al, 2022) and implementing time series classifiers (Mirmazloumi et al, 2022).…”

Differential Sar Interferometry for the Monitoring of Land Subsidence Along Railway Infrastructures

“…𝛼 0 , 𝛽 0 and 𝛽 1 stands for thresholds to be determined by operators. Detailed formulation of procedures for the quality score assignment is presented inWassie et al (2022).…”