Spatio-temporal quality indicators for differential interferometric Synthetic Aperture Radar data

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.This research was partially funded by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), Generalitat de Catalunya—through a grant for the recruitment of early-stage research staff (Ref: 2021FI_B2 00186).Peer ReviewedPostprint (published version

Interferometric SAR deformation timeseries: a quality index

Estimating unknown absolute phase from a wrapped observation is a challenging and ill-posed problem that possibly leads to misinterpretation of interferometric SAR (InSAR) deformation results. In this study, we introduce a quality index to cluster post-phase unwrapping multi-master InSAR timeseries outputs based on the estimated phase residuals and redundancy of network of interferograms. The index is supposed to indicate the reliability of a timeseries, including the identification of persistent scatterers (PSs) possibly affected by phase unwrapping jumps. The algorithm was tested on two Sentinel-1 interferometric datasets with 622,991 and 95,398 PSs, generated from the PSI processing chain PSIG of the geomatics division of CTTC. Promising result have been achieved-especially in identifying erroneous PSs with phase unwrapping jumps. Along with existing temporal phase consistency checking algorithms, the approach could provide rich information toward a better interpretation of the deformation timeseries results.This work has been funded by AGAUR, Generalitat de Catalunya, in the framework of Resolution EMC/ 2459/2019, FI-2020.Peer ReviewedPostprint (published version

Ground movement classification using statistical tests over persistent scatterer interferometry time series

This study proposes modifications to an existing automatic classification method of Persistent Scatterers Interferometry (PSI) time series (TS) and a new procedure to classify ground movements into seven classes. We also represent a technique to detect TSs affected by phase unwrapping errors and a reclassification part to detect stable points, which are incorrectly classified as moving points using the original method. Around 60 km2 of Catalunya were classified using Sentinel-1 images and a PSI technique. The proposed method classified 78359 PS TS. This study provided the spatial distribution of ground movement classes and detected several time series anomalies.Peer ReviewedPostprint (published version

InSAR deformation time series classification using a convolutional neural network

Temporal analysis of deformations Time Series (TS) provides detailed information of various natural and humanmade displacements. Interferometric Synthetic Aperture Radar (InSAR) generates millimetre-scale products, indicating the chronicle behaviour of detected targets via TS products. Deep Learning (DL) can handle a massive load of InSAR TS to categorize significant movements from non-moving targets. To this end, we employed a supervised Convolutional Neural Network (CNN) model to distinguish five deformations trends, including Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error (PUE). Considering several arguments in a CNN model, we trained numerous combinations to explore the most accurate combination from 5000 samples extracted from a Persistent Scatterer Interferometry (PSI) technique and Sentinel-1 images over the Granada region, Spain. The model overall accuracy exceeds 92%. Deformations of three cases of landslides were also detected over the same area, including the Cortijo de Lorenzo, El Arrecife, and Rules Viaduct areas.Peer ReviewedPostprint (published version

Supervised machine learning algorithms for ground motion time series classification from InSAR data

The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the genera- tion of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deforma- tion identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS.This work is part of the Spanish Grant SARAI, PID2020-116540RB-C21, funded by MCIN/ AEI/10.13039/501100011033. Additionally, it has been supported by the European Regional Devel- opment Fund (ERDF) through the project “RISKCOAST” (SOE3/P4/E0868) of the Interreg SUDOE Programme. Additionally, this work has been co-funded by the European Union Civil Protection through the H2020 project RASTOOL (UCPM-2021-PP-101048474).Peer ReviewedPostprint (published version

Sentinel-1 A-DInSAR approaches to map and monitor ground displacements

Persistent scatterer interferometry (PSI) is a group of advanced interferometric synthetic aperture radar (SAR) techniques used to measure and monitor terrain deformation. Sentinel-1 has improved the data acquisition throughout and, compared to previous sensors, increased considerably the differential interferometric SAR (DInSAR) and PSI deformation monitoring potential. The low density of persistent scatterer (PS) in non-urban areas is a critical issue in DInSAR and has inspired the development of alternative approaches and refinement of the PS chains. This paper proposes two different and complementary data-driven procedures to obtain terrain deformation maps. These approaches aim to exploit Sentinel-1 highly coherent interferograms and their short revisit time. The first approach, called direct integration (DI), aims at providing a very fast and straightforward approach to screen-wide areas and easily detects active areas. This approach fully exploits the coherent interferograms from consecutive images provided by Sentinel-1, resulting in a very high sampling density. However, it lacks robustness and its usability lays on the operator experience. The second method, called persistent scatterer interferometry geomatics (PSIG) short temporal baseline, provides a constrained application of the PSIG chain, the CTTC approach to the PSI. It uses short temporal baseline interferograms and does not assume any deformation model for point selection. It is also quite a straightforward approach, which improves the performances of the standard PSIG approach, increasing the PS density and providing robust measurements. The effectiveness of the approaches is illustrated through analyses performed on different test sites.This work has been partially funded by AGAUR, Generalitat de Catalunya, through a grant for the recruitment of early-stage research staff (Ref: FI_B 00741) and through the Consolidated Research Group RSE, “Remote Sensing” (Ref: 2017-SGR-00729). It has been also partially funded by the Spanish Ministry of Economy and Competitiveness through the DEMOS project “Deformation monitoring using Sentinel-1 data” (Ref: CGL2017-83704-P) and by AGAUR.Peer ReviewedPostprint (published version

Analysis of the products of the Copernicus ground motion service

Radar interferometry has progressed very much in the last two decades. It is now a powerful remote sensing techniques to monitor ground motion. The technique has undergone an important development in terms of processing and data analysis algorithms. This has been accompanied by an important increase of the Synthetic Aperture Radar (SAR) data acquisition capability by spaceborne sensors. A step forward was the launch of the Copernicus Sentinel-1 constellation. This has made the development of A-DInSAR (Advanced Differential Interferometric SAR) ground deformation services technically feasible. The paper is focused on the most important ground motion initiative ever conceived: the European Ground Motion Service (EGMS). This service is part of the Copernicus Land Monitoring Service managed by the European Environment Agency. EGMS involves the ground deformation monitoring at European scale. The service will deliver the first product in May 2022. In this paper we describe some preliminary examples of deformation products coming from the EGMS.This work is part of the Spanish Grant SARAI, PID2020- 116540RB-C21, which has been funded by the MCIN/AEI/ 10.13039/501100011033. The A-DInSAR results shown in this paper belong to the EGMSPeer ReviewedPostprint (published version

Active reflectors for interferometric SAR deformation measurement

This paper is focused on the design, implementation and testing of an active reflector, to be used to support deformation monitoring studies based on Synthetic Aperture Radar interferometry. The device is designed to work in C-band with Sentinel-1 data, operating at 5.405 GHz ± 50 MHz. A brief description of the active reflector is provided. It consists of two antennas and an amplifying section. The active reflector has been tested in different experiments. In this paper, we describe the experiment carried out in the Parc Mediterrani de la Tecnologia (Castelldefels, Barcelona). The result shows a strong correlation with temperature. A calibration test was carried out to experimentally derive a calibration curve to correct the effect of temperature on phase stability.This work is part of a project that has received funding from the European GNSS Agency under the European Union’s Horizon 2020 research and innovation programme, with grant agreement No 776335, project GIMS, “Geodetic Integrated Monitoring System”. This work has been partially funded by AGAUR, Generalitat de Catalunya, through the Consolidated Research Group RSE, “Remote Sensing” (Ref: 2017-SGR-00729)Postprint (published version