Ground-based Synthetic Aperture Radar Interferometry Research Articles

A 2D-PRM-Based Atmospheric Phase Correction Method in GB-SAR Interferometry Application

Ground-based synthetic aperture radar (GB-SAR) interferometry has already been recognized as a powerful tool for remote sensing deformation monitoring. However, the accuracy and reliability of the deformation retrieval using GB-SAR instrument is severely affected by atmospheric phase disturbances. In this paper, an improved two-dimentional polynomial regression model (2D-PRM) based atmospheric phase compensation (APC) algorithm, in collaboration with the permanent scatterer (PS) technique, is developed in GB-SAR interferometry. In order to obtain the aliasing-free data required for the APC procedure, an efficient two-stage spatial phase unwrapping method is utilized. Then the reliable PS points are selected by jointly exploiting the amplitude, coherence and amplitude deviation index (ADI) information. After that, the appropriate APC is carried out by virtue of a 2D-PRM based least square (LS) method. Finally, we conduct the atmospheric correction experiments on a real campaign to verify the performance of the proposed scheme.

A Deep Learning Application for Deformation Prediction from Ground-Based InSAR

Ground-based synthetic aperture radar interferometry (GB-InSAR) has the characteristics of high precision, high temporal resolution, and high spatial resolution, and is widely used in highwall deformation monitoring. The traditional GB-InSAR real-time processing method is to process the whole data set or group in time sequence. This type of method takes up a lot of computer memory, has low efficiency, cannot meet the timeliness of slope monitoring, and cannot perform deformation prediction and disaster warning forecasting. In response to this problem, this paper proposes a GB-InSAR time series processing method based on the LSTM (long short-term memory) model. First, according to the early monitoring data of GBSAR equipment, the time series InSAR method (PS-InSAR, SBAS, etc.) is used to obtain the initial deformation information. According to the deformation calculated in the previous stage and the atmospheric environmental parameters monitored, the LSTM model is used to predict the deformation and atmospheric delay at the next time. The phase is removed from the interference phase, and finally the residual phase is unwrapped using the spatial domain unwrapping algorithm to solve the residual deformation. The predicted deformation and the residual deformation are added to obtain the deformation amount at the current moment. This method only needs to process the difference map at the current moment, which greatly saves time series processing time and can realize the prediction of deformation variables. The reliability of the proposed method is verified by ground-based SAR monitoring data of the Guangyuan landslide in Sichuan Province.

Microdeformation monitoring by permanent scatterer GB-SAR interferometry based on image subset series with short temporal baselines: The Geheyan Dam case study

Deformation measurement precision of ground-based synthetic aperture radar (GB-SAR) is significantly limited by atmospheric disturbances. To separate the atmospheric and time domain low-pass deformations in high-time-sampling-rate image sequences, an improved permanent scatterer (PS) GB-SAR interferometry method is proposed to monitor microdeformation. The interferometric analysis is based on PSs and image subset series with short temporal baselines. A polynomial atmospheric correction method is adopted to remove the main atmospheric phase of the interferograms and adjust the interferometric phase model accordingly. The linear deformation rates are estimated by a quasi-stable adjustment method guaranteeing the internal coincidence accuracy of the estimation. Spatiotemporal filtering is implemented on the residual phase to obtain the remaining nonlinear deformation to complete the deformation sequence. An experiment was conducted on the Geheyan Dam, China, verifying the GB-SAR results with plummet data. The RMSEs calculated between the deformation series by the two methods were better than 0.30 mm.

Joint exploitation of space-borne and ground-based multitemporal InSAR measurements for volcano monitoring: The Stromboli volcano case study

We present a joint exploitation of space-borne and ground-based Synthetic Aperture Radar Interferometry (InSAR) and Multi Temporal (MT) InSAR measurements for investigating the Stromboli volcano (Italy) deformation phenomena. In particular, we focus our analysis on three periods: a) the time interval following the 2014 flank eruption, b) the July–August 2019 eruption and c) the following post-eruptive phase. To do this, we take advantage from an unprecedented set of space-borne and ground-based SAR data collected from April 2015 up to November 2019 along two (one ascending and one descending) Sentinel-1 (S-1) tracks, as well as, in the same period, by two ground-based systems installed along the Sciara del Fuoco northern rim. Such data availability permitted us to first characterize the volcano long-term 3D deformation behavior of the pre-eruptive period (April 2015–June 2019), by jointly inverting the space-borne and ground-based InSAR measurements. Then, the GB-SAR measurements allowed us to investigate the sin-eruptive time span (3rd July 2019 – 30th August 2019) which revealed rapid deformation episodes (e.g. more than 30 mm/h just 2 min before the 3rd July 2019 explosion) associated with the eruptive activity, that cannot be detected with the weekly S-1 temporal sampling. Finally, the S-1 measurements permitted to better constrain the post 2019 eruption deformations (31st August 2019 – 5th November 2019), which are mainly located outside the GB-SAR sensed area. The presented results demonstrate the effectiveness of the joint exploitation of the InSAR measurements obtained through satellite and terrestrial SAR systems, highlighting their strong complementarity to map and interpret the deformation phenomena affecting volcanic areas.

Geological monitoring networks for risk management close to large rock cliffs: the case history of Gallivaggio and Cataeggio in the italian Alps

Abstract. In areas located near large rock cliffs, risk reduction by early warning monitoring systems highligts potentiality but also critical issues and limits. The paper examines two rock slope failures that occurred in a short time from each other near inhabited areas in the Italian Alps. The viscous behavior of the rock mass was reconstructed through data processing from ground-based Synthetic Aperture Radar Interferometry (InSAR), and elaboration of acceleration and speed curves. Landslides types and underlying complexity associated with rock detachment mechanisms suggest the identification of precautionary alarm thresholds for collapse forecasting. The analysis of financial outlay, both for mitigation works and for monitoring activities, highlight the adequacy and the opportunity to combine passive systems, like embankments or rockfall drapery meshes, with a reliable monitoring network for early warning.

Terrain Point Cloud Assisted GB-InSAR Slope and Pavement Deformation Differentiate Method in an Open-Pit Mine.

Ground-based synthetic aperture radar interferometry (GB-InSAR) is a valuable tool for deformation monitoring. The 2D interferograms obtained by GB-InSAR can be integrated with a 3D terrain model to visually and accurately locate deformed areas. The process has been preliminarily realized by geometric mapping assisted by terrestrial laser scanning (TLS). However, due to the line-of-sight (LOS) deformation monitoring, shadow and layover often occur in topographically rugged areas, which makes it difficult to distinguish the deformed points on the slope between the ones on the pavement. The extant resampling and interpolation method, which is designed for solving the scale difference between the point cloud and radar pixels, does not consider the local scattering characteristics difference of slope. The scattering difference information of road surface and slope surface in the terrain model is deeply weakened. We propose a differentiated method with integrated GB-InSAR and terrain surface point cloud. Local geometric and scattering characteristics of the slope were extracted, which account for pavement and slope differentiating. The geometric model is based on a GB-InSAR system with linear repeated-pass and the topographic point cloud relative observation geometry. The scattering model is based on k-nearest neighbor (KNN) points in small patches varies as radar micro-wave incident angle changes. Simulation and a field experiment were conducted in an open-pit mine. The results show that the proposed method effectively distinguishes pavement and slope surface deformation and the abnormal area boundary is partially relieved.

Reservoir Dam Surface Deformation Monitoring by Differential GB-InSAR Based on Image Subsets.

Ground-based synthetic aperture radar interferometry (GB-InSAR) enables the continuous monitoring of areal deformation and can thus provide near-real-time control of the overall deformation state of dam surfaces. In the continuous small-scale deformation monitoring of a reservoir dam structure by GB-InSAR, the ground-based synthetic aperture radar (GB-SAR) image acquisition may be interrupted by multiple interfering factors, such as severe changes in the meteorological conditions of the monitoring area and radar equipment failures. As a result, the observed phases before and after the interruption cannot be directly connected, and the original spatiotemporal datum for the deformation measurement is lost, making the follow-up monitoring results unreliable. In this study, a multi-threshold strategy was first adopted to select coherent point targets (CPTs) by using successive GB-SAR image sequences. Then, we developed differential GB-InSAR with image subsets based on the CPTs to solve the dam surface deformation before and after aberrant interruptions. Finally, a deformation monitoring experiment was performed on an actual large reservoir dam. The effectiveness and accuracy of the abovementioned method were verified by comparing the results with measurements by a reversed pendulum monitoring system.

A Multiple-Regression Model Considering Deformation Information for Atmospheric Phase Screen Compensation in Ground-Based SAR

The accuracy of ground-based synthetic aperture radar (GB-SAR) interferometry is highly affected by the atmospheric phase screen (APS). A multiple-regression model (MRM) for APS estimation with two parameters (line of sight (LOS) distance and the height difference between the targets and the GB-SAR sensor) was proposed. However, this MRM is inapplicable if the LOS deformation of the observed objects has similar behavior to that of the APS. For example, both the APS and the LOS deformation of observed objects can have an increasing trend along the LOS direction. In this article, an improved MRM (IMRM) that considers the spatial pattern of the LOS deformation of observed objects was proposed. The spatial pattern can be expressed by the location information of the observed objects. The applicability of this improved model was demonstrated in a deformation-measured campaign carried out on a high loess cut slope using 285 images obtained by the GB-SAR sensor during a three-day measurement. The similarity and dissimilarity between the APS and the behavior of the LOS deformation of the slope were illustrated and used to estimate the APS. The accuracy of this IMRM was verified using deformation data measured by a displacement meter. This IMRM will be helpful in APS estimation and compensation in GB-SAR interferometry in case studies where the APS and the LOS deformation of observed objects have similar behavior.

An Improved Atmospheric Phase Compensation Approach to Ground-based SAR Interferometry for Landslide Monitoring

Ground-based synthetic aperture radar (GB-SAR) interferometry is an increasingly popular terrestrial remote sensing technique for landslide monitoring. However, in actual application, the atmospheric phase screen (APS) seriously affects the accuracy of GB-SAR interferometry. Therefore, it is necessary to estimate the APS accurately. This paper develops an improved APS compensation method based on a 2D-polynomial regression model (2D-PRM) with a two-step estimation process using GB-SAR measurements. In this paper, permanent scatterer (PS) points which are selected by a joint method using amplitude, amplitude deviation and correlation coefficient are used to roughly estimate the model parameters. Then, by imposing the condition of the standard deviation of the fitting error, the outliers whose values enormously affect the estimation of parameters are filtered out of the estimation process. A more efficient estimation of the APS is finally achieved by using the remaining pixels based on 2D-PRM. At last, a landslide monitoring experiment is carried out to verify the performance of the proposed method.

High-quality pixel selection in ground-based SAR interferometry based on pseudo- and quasi-permanent scatterers analyses

Due to the presence of phase noise and temporal decorrelation, high-quality pixel selection has been an indispensable part in ground-based synthetic aperture radar (GB-SAR) interferometry. The main pixel selection methods are permanent scatterer (PS) methods and coherent scatterer methods. However, both of them cannot properly solve the false-selection and the miss-selection caused by intrinsic scattering and temporal characteristics of the pixel on the whole dataset. Pseudo-PS and quasi-PS are first introduced into the high-quality pixel selection of GB-SAR interferometry, and a temporal selection method (TSM) is proposed to avoid the miss-selection of quasi-PS. Then, in order to achieve appropriate pixel density and high-quality pixels for deformation monitoring, a high-quality interferometric pixel selection method based on pseudo- and quasi-permanent scatterers analyses is proposed, which is a joint processing technique combining spatial variant apodization with TSM. Finally, the effectiveness of the proposed high-quality pixel selection method is validated by the field measurements of GB-SAR data.

GB-SAR Interferometry Based on Dimension-Reduced Compressive Sensing and Multiple Measurement Vectors Model

To reduce the data acquisition time and the high-level sidelobes produced by conventional focusing methods for ground-based synthetic aperture radar interferometry, we present a new method to provide accurate displacement maps based on the dimension-reduced compressive sensing (CS) method combined with the multiple measurement vectors (MMVs) model. The proposed CS method consists in selecting the supported area of targets, estimated by the fast conventional method with undersampled data. The following sparse reconstruction is applied only to the selected areas. The MMV-based approach allows increasing the coherence and the precision of displacement estimates. Two experiments are carried out to assess the performance of the proposed method.

An efficient method of monitoring slow-moving landslides with long-range terrestrial laser scanning: a case study of the Dashu landslide in the Three Gorges Reservoir Region, China

Accurate measurement of the small pre-failure deformation plays a crucial role in landslide early warning systems. Recently, terrestrial laser scanning (TLS) has been applied to detect the small pre-failure deformation, while the processing of TLS point clouds is still a non-trivial issue, especially for long-range TLS. Therefore, we propose an efficient method to detect the small pre-failure deformation of slow-moving landslides by the use of long-range TLS. To precisely align the point clouds of different epochs, a hybrid-weighted iterative closest point (HWICP) algorithm is proposed after the coarse registration, where the influences of several factors (i.e., deformed areas, point density variations, and observation errors) on the inconsistency of the point correspondence are quantitatively estimated. An adaptive local cloud-to-mesh method (ALC2M) is then proposed for the deformation calculation in the complex topography by defining a local reference frame and removing poor matches between ground points of the two epochs. To validate the performance of the proposed method, the Dashu landslide of the Three Gorges Reservoir Region was selected as a case study. The TLS-derived deformation showed that the landslide was stable in most areas over the study period, except for two abnormal deformation zones with maximum deformations of ~ 6 cm and ~ 3 cm, which agreed well with the ground-based synthetic aperture radar interferometry (GB-InSAR) measurements. A statistical analysis of the stable areas around the landslide illustrated a low uncertainty for the TLS-derived deformation of 6.3 mm. Therefore, the use of the long-range TLS was an efficient way to monitor slow-moving landslides. Moreover, extensive comparative experiments about the co-registration and deformation calculation were conducted, and the results showed that HWICP and ALC2M achieved superior performances.

Integrated Ground-Based SAR Interferometry, Terrestrial Laser Scanner, and Corner Reflector Deformation Experiments

An integrated sensor system comprised of a terrestrial laser scanner (TLS), corner reflectors (CRs), and high precision linear rail is utilized to validate ground-based synthetic aperture radar (GB-SAR) interferometric micro-displacement measurements. A rail with positioning accuracy of 0.1 mm is deployed to ensure accurate and controllable deformation. The rail is equipped with a CR on a sliding platform for mobility. Three smaller CRs are installed nearby, each with a reflective sticker attached to the CR’s vertex; the CRs present as high-amplitude points both in the GB-SAR images and the TLS point cloud to allow for accurate data matching. We analyze the GB-SAR zero-baseline repeated rail differential interferometry signal model to obtain 2D interferograms of the test site in time series, and then use TLS to obtain a 3D surface model. The model is matched with interferograms to produce more intuitive 3D products. The CR displacements can also be extracted via surface reconstruction algorithm. Finally, we compared the rail sensor measurement and TLS results to optimize coherent scatterer selection and filter the data. The proposed method yields accurate target displacement results via quantitative analysis of GB-SAR interferometry.

A New Nonlocal Method for Ground-Based Synthetic Aperture Radar Deformation Monitoring

Ground-based synthetic aperture radar (GBSAR) interferometry offers an effective solution for the monitoring of surface displacements with high precision. However, coherence estimation and phase filtering in GBSAR interferometry is often based on a rectangular window, resulting in estimation biases and resolution loss. To address these issues, conventional nonlocal methods developed for spaceborne synthetic aperture radar are investigated with GBSAR data for the first time. Based on investigation and analysis, an efficient similarity measure is proposed to identify pixels with similar amplitude behaviors and a comprehensive nonlocal method is presented on the basis of this concept with the aim of overcoming current limitations. Pixels with high similarity are identified from a large search window for each point based on a stack of GBSAR single look complex images. Coherence is calculated based on the selected sibling pixels and then enhanced by the second kind statistic estimator. Nonlocal means filtering is also performed based on the sibling pixels to reduce interferometric phase noise. Experiments were conducted using short- and long-term GBSAR interferograms. Qualitative and quantitative analyses of the proposed nonlocal method and other existing techniques demonstrate that the new approach has advantages in terms of coherence estimation and phase filtering capability. The proposed method was integrated into a complete GBSAR small baseline subset algorithm and a time series analysis was achieved for two stacks of data sets. Considered alongside experimental results, this successful application demonstrates the feasibility of the proposed nonlocal method to facilitate the adoption of GBSAR for deformation monitoring applications.

Ground-based synthetic aperture radar interferometry for deformation monitoring: a case study at Geheyan Dam, China

Full coverage and continuous deformation information retrieval are key aspects for dam health diagnosis. Ground-based synthetic aperture radar (GB-SAR) interferometry is used for the remote monitoring of the Geheyan Dam, China. Although the monitoring of a dam with ground-based interferometry is not an innovation, specific issues have been found out in the case study discussed due to the large dimension of the monitored structure. More than 400 images were used for interferogram generation. Radar signals reflected from the dam were carefully analyzed: a sort of tunneling effect caused by multireflection is observed, and deformations caused by water level and temperature variations were detected during a six-day monitoring campaign. Radar monitoring results were compared to the data recorded by plummets installed in the dam. The agreement between the displacements retrieved from interferometric data and the plummets demonstrates the capability of GB-SAR for deformation monitoring, with the advantage of large area coverage.

A Compact Ground-Based Interferometric Radar for Landslide Monitoring: The Xerém Experiment

A real-time monitoring system based on ground-based synthetic aperture radar interferometry (GB-InSAR) were developed and deployed with the objective to gain understanding of landslide dynamics in an area in Xerém (district of Duque de Caxias - RJ, Brazil). The area is subjected to landslide risk and the deployed system aims to provide subsidies to the authorities to prepare actions related to prevention and evacuation, and serve as the basis for a future early-warning system in the framework of the Civil Defense of Duque de Caxias. The system consists of a compact and rugged X-band GB-InSAR newly developed by Bradar, Brazil. The processing and monitoring methodology are proposed. Two campaigns, totaling six days, were carried out. The acquisitions of the first campaign occurred without rain. In the second campaign, rain events, varying from light to torrential rainfall, occurred for a period of time, in subsequent days, during the radar acquisitions. New insights on landslide dynamics are revealed. The monitoring data show that, approximately 10 h after heavy rainfall, the terrain movement on a slope reached 4 mm/h during ca. 5 to 8 h, resulting in velocities of 20 to 32 mm/day.

Atmospheric phase correction using permanent scatterers in ground-based radar interferometry

Ground-based synthetic aperture radar (GBSAR) is a powerful tool used in monitoring structures such as bridges and dams. However, despite the extremely short range of GBSAR interferometry, the atmospheric effects cannot be neglected. The permanent scatterer (PS) technique is an effective operational tool that utilizes a long series of SAR data and detects information with high accuracy. An algorithm based on the PS technique is developed in accordance with the phase model used in GBSAR interferometry. Atmospheric correction is carried out on a real campaign (Geheyan Dam, China). The atmospheric effects created using this method, which utilizes SAR data, can be effectively reduced compared to when plumb line data are used.

Atmospheric phase screen correction in ground-based SAR with PS technique.

Ground-based synthetic aperture radar (GBSAR) is a powerful tool used in monitoring structures, such as bridges and dams. However, despite the extremely short range of GBSAR interferometry, the atmosphere effects cannot be neglected. The permanent scatterer technique is an effective operational tool that utilizes a long series of SAR data and detects information with high accuracy. An algorithm based on the permanent scatterer technique is developed in accordance with the phase model used in GBSAR interferometry. In this study, atmospheric correction is carried out on a real campaign (Geheyan Dam, China). The atmosphere effects created using this method, which utilizes SAR data, can be reduced effectively compared to when plumb line data are used.

Early Warning GBInSAR-Based Method for Monitoring Volterra (Tuscany, Italy) City Walls

Ground-based synthetic aperture radar interferometry (GBInSAR) remote sensing technique has been repeatedly proved an effective tool for monitoring built environment affected by structural and geological criticalities. In this paper, it is described how this technique can be successfully applied for early-warning procedures and detection of ongoing deterioration processes on archeological and cultural heritage sites. An integrated approach of GBInSAR and terrestrial laser scanner (TLS) technologies was performed on Volterra test site (Tuscany, Italy), where a sudden collapse of a 35-m wide section of city walls occurred on January 31, 2014. The installed early-warning monitoring system is capable of an accurate and focused real-time displacement detection of the south-western side of the city including walls, buildings, and monuments, thus allowing prompt interventions for citizens safety and conservation purposes. The effectiveness of this alert technique became evident when the precursors of a second impressive wall collapse were clearly detected. From the beginning of the GBInSAR monitoring, we measured a constant displacement velocity of 0.1 mm/h in correspondence to a 15-m high wall sustaining the Acropolis and lying an underground parking. After a sudden increase of velocity values up to 1.7 mm/h, the local authorities were alerted so that they had time to interdict the area to citizens and to take adequate safety countermeasures two days before the collapse.

Integration between ground based and satellite SAR data in landslide mapping: The San Fratello case study

The potential use of the integration of PSI (Persistent Scatterer Interferometry) and GB-InSAR (Ground-based Synthetic Aperture Radar Interferometry) for landslide hazard mitigation was evaluated for mapping and monitoring activities of the San Fratello landslide (Sicily, Italy). Intense and exceptional rainfall events are the main factors that triggered several slope movements in the study area, which is susceptible to landslides, because of its steep slopes and silty–clayey sedimentary cover.In the last three centuries, the town of San Fratello was affected by three large landslides, developed in different periods: the oldest one occurred in 1754, damaging the northeastern sector of the town; in 1922 a large landslide completely destroyed a wide area in the western hillside of the town. In this paper, the attention is focussed on the most recent landslide that occurred on 14 February 2010: in this case, the phenomenon produced the failure of a large sector of the eastern hillside, causing severe damages to buildings and infrastructures. In particular, several slow-moving rotational and translational slides occurred in the area, making it suitable to monitor ground instability through different InSAR techniques.PS-InSAR™ (permanent scatterers SAR interferometry) techniques, using ERS-1/ERS-2, ENVISAT, RADARSAT-1, and COSMO-SkyMed SAR images, were applied to analyze ground displacements during pre- and post-event phases. Moreover, during the post-event phase in March 2010, a GB-InSAR system, able to acquire data continuously every 14min, was installed collecting ground displacement maps for a period of about three years, until March 2013. Through the integration of space-borne and ground-based data sets, ground deformation velocity maps were obtained, providing a more accurate delimitation of the February 2010 landslide boundary, with respect to the carried out traditional geomorphological field survey. The integration of GB-InSAR and PSI techniques proved to be very effective in landslide mapping in the San Fratello test site, representing a valid scientific support for local authorities and decision makers during the post-emergency management.