Permanent Scatterer Interferometry Research Articles

Mapping of Land Surface Deformation Using Ps-Insar for Disaster Risk Management in the Future

DKI Jakarta is experiencing land subsidence due to overexploitation of its use and the increasing population. It is feared that this decline or deformation will occur in the location of the new national capital. The research objective is "Mapping of Land Surface Deformation using PS-InSAR for Disaster Risk Management in the Future." Quantitative and qualitative research and data collection methods use secondary and primary data. Secondary data in the form of Permanent Scatterers Interferometry Synthetic Aperture Radar (PS-InSAR) Sentinel-1A images to determine soil deformation. Primary data uses a questionnaire to assess disaster risk management. Data analysis uses spatial and statistical analysis. Spatial analysis for land deformation mapping and statistical analysis for risk management. The results showed that the pattern of land deformation before the determination of the location of the capital city of Indonesia was random. On the other hand, after decision-making, it appears to be more systematic and homogeneous in adjacent areas with a decreasing range of about 5 cm per year. Other findings show that disaster risk management carried out by several agencies, especially the problem of land deformation in East Kalimantan, is still far from expectations and very minimal. The findings can be used for future disaster risk management to minimize negative impacts and reduce disaster risk.Keywords: PS-INSAR; Land Deformation; Capital City; Disaster Risk Management

Calculating of Taftan Volcano Displacement Using PSI Technique and Sentinel 1 Images

Abstract. Taftan is a semi-active volcano located in southeast of Iran with a number of craters. The main objective of this study is to investigate whether subsidence or uplift in Taftan peak. A total number of 58 images of Sentinel 1-A acquired between January 2015 to December 2020 in the ascending orbit mode and 102 images of both Sentinel 1-A, Sentinel 1-B acquired between October 2014 to June 2020 in the descending orbit mode were pre-processed for this purpose. The interferograms with the permanent scatterer interferometry (PSI) method were created using SARPROZ and StaMPS software in which atmospheric corrections were made automatically and following that get surface displacement of Taftan volcano. The results of the Line Of Sight (LOS) displacement corresponding to the uplift was observed to be 0.5 mm to 1 mm yr-1 for the ascending orbit and 1 mm yr-1 for the descending orbit. Because of GPS station lack close to Taftan volcano, the GPS measurements of one station located in the study area (Saravan station) was used to check the accuracy of PSI method, the GPS station of SARAVAN has been located inside of town and it is appropriate to use and analyze PSI technique in this station. As a result, it was found that the PSI method is in good agreement with the GPS data.

Multi-Temporal PSI Analysis and Burn Severity Combination to Determine Ground-Burned Hazard Zones

Forest fires are a seasonal phenomenon in Greece, reoccurring annually and causing adverse impacts on both human-made and natural environments. Our case study focuses on the devastating fire that took place in July 2018 in the second-housing area of Mati, East Attica. In this research, we propose a simple and effective approach that combines the deformation trend obtained from the Permanent Scatterer Interferometry (PSI) analysis with the burn severity assessment aiming to identify and classify potential ground-burn hazard zones. To maximize the number of measuring points, we employ a weighted full-graph PSI approach. Additionally, we calculate the burn severity by comparing Sentinel-2 satellite images captured before and after the event. The resulting datasets are reclassified on a scale from 1 to 5, and the proposed equation yields the final product. Numerous high and very high hazard zones have been identified using this methodology. The research findings reveal the proximity between these hazard zones and the stream network. Overall, the proposed method offers valuable insights for the post-fire monitoring and management of urban and peri-urban landscapes in the affected areas.

Monitoring and prediction of landslide-related deformation based on the GCN-LSTM algorithm and SAR imagery

A key component of disaster management and infrastructure organization is predicting cumulative deformations caused by landslides. One of the critical points in predicting deformation is to consider the spatio-temporal relationships and interdependencies between the features, such as geological, geomorphological, and geospatial factors (predisposing factors). Using algorithms that create temporal and spatial connections is suggested in this study to address this important point. This study proposes a modified graph convolutional network (GCN) that incorporates a long and short-term memory (LSTM) network (GCN-LSTM) and applies it to the Moio della Civitella landslides (southern Italy) for predicting cumulative deformation. In our proposed deep learning algorithms (DLAs), two types of data are considered, the first is geological, geomorphological, and geospatial information, and the second is cumulative deformations obtained by permanent scatterer interferometry (PSI), with the first investigated as features and the second as labels and goals. This approach is divided into two processing strategies where: (a) Firstly, extracting the spatial interdependency between paired data points using the GCN regression model applied to velocity obtained by PSI and data depicting controlling predisposing factors; (b) secondly, the application of the GCN-LSTM model to predict cumulative landslide deformation (labels of DLAs) based on the correlation distance obtained through the first strategy and determination of spatio-temporal dependency. A comparative assessment of model performance illustrates that GCN-LSTM is superior and outperforms four different DLAs, including recurrent neural networks (RNNs), gated recurrent units (GRU), LSTM, and GCN-GRU. The absolute error between the real and predicted deformation is applied for validation, and in 92% of the data points, this error is lower than 4 mm.

Slow Deformation Time-Series Monitoring for Urban Areas Based on the AWHPSPO Algorithm and TELM: A Case Study of Changsha, China

Health monitoring is important for densely distributed urban infrastructures, particularly in cities undergoing rapid economic progress. Permanent scatterer interferometry (PSI) is an advanced remote sensing observation technique that is commonly used in urban infrastructure monitoring. However, the rapid construction of infrastructures may easily cause a loss of coherence for radar interferometry, inducing a low density of effective permanent scatterer (PS) points, which is the main limitation of PSI. In order to address these problems, a novel time-series synthetic aperture radar interferometry (InSAR) process based on the adaptive window homogeneous pixel selection and phase optimization (AWHPSPO) algorithm and thermal expansion linear model (TELM) is proposed. Firstly, for homogeneous point selection, information on both the time-series intensity and deformation phases is considered, which can compensate for the defects of insufficient homogeneous samples and low phase quality in traditional distributed scatterer interferometric synthetic aperture radar (DS-InSAR) processing. Secondly, the physical, thermal expansion component, which reflects the material properties of the infrastructures, is introduced into the traditional linear model, which can more rationally reflect the temporal evolution of deformation variation, and the thermal expansion coefficients can be estimated simultaneously with the deformation parameters. In order to verify our proposed algorithm, the Orange Island area in Changsha City, China, was selected as the study area in this experiment. Three years of its historical time-series deformation fields and thermal expansion coefficients were regenerated. With the use of high-resolution TerraSAR-X radar satellite images, a maximum accumulated settlement of 12.3 mm and a minor uplift of 8.2 mm were detected. Crossvalidation with small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) results using Sentinel 1A data proved the reliability of AWHPSPO. The proposed algorithm can provide a reference for the control of the health and safety of urban infrastructures.

Investigating ground deformation due to a series of collapse earthquakes by means of the PS-InSAR technique and Sentinel 1 data in Kandy, Sri Lanka

Time series interferometry synthetic aperture radar (InSAR) techniques have rarely been applied to detect displacement due to low-magnitude (5.5 ≥ M) earthquakes. This study exploits the combined permanent scatterer (PS) interferometry (PS-InSAR) and differential interferometry (D-InSAR) methods to investigate the deformation rates due to low-magnitude partial series of collapse earthquakes. Kandy, the hill capital of Sri Lanka, was experiencing a series of collapsed earthquakes. Historical geological evidence of dynamic topography with prolonged vertical movement further promotes the initiatory InSAR investigation. A series of Sentinel 1A images from the years 2019 to 2021 were employed. Initial D-InSAR near-zero baseline analysis suggested possible displacement in the range of −10 mm / year and +2 mm / year for subsidence and uplift, respectively. Using these prior motion velocities, the temporal coherence was optimized in PS-InSAR. Variogram models and ordinary Kriging (OK) were employed to predict deformation for the areas with limited PS detection. Descending orbits images PS show a dominant uplift of +20 mm / year, which are concentrated over Kandy urban areas. Movements along the ascending line of sight at Victoria Dam in the south are in the range of −40 mm / year. Slopes along the Mahaweli river lineament to the east show subsidence in the range of −29 to 36 mm / year. The coregistered landslide hazard map of Kandy shows deformation areas are exposed to landslide risk. Changes in terrestrial and ground water levels measured with the gravity recovery and climate experiment/GRACE-Follow-On during the period reveal significant irregularities. The study can be considered a prototype example that can be extended to investigate low-magnitude incomplete collapse earthquakes in different geological and geotechnical setups for ground deformation.

Mapping of Mean Deformation Rates Based on APS-Corrected InSAR Data Using Unsupervised Clustering Algorithms

Different interferometric approaches have been developed over the past few decades to process SAR data and recover surface deformation, and each approach has advantages and limitations. Finding an accurate and reliable interval for preparing mean deformation rate maps (MDRMs) remains challenging. The primary purpose of this paper is to implement an application consisting of three unsupervised clustering algorithms (UCAs) for determining the best interval from SAR-derived deformation data, which can be used to interpret long-term deformation processes, such as subsidence, and identify displacement patterns. Considering Port Harcourt (in the Niger Delta) as the study area, it was essential to remove the sources of error in extracting deformation signals from SAR data, spatially ionospheric and tropospheric delays, before using UCAs to obtain its characteristics and real deformation data. Moreover, another purpose of this paper is to implement the advanced integration method (AIM) for atmospheric phase screen (APS) correction to enhance deformation signals obtained through different SAR processing approaches, including interferometric SARs (two-pass interferometry, InSAR) and multitemporal interferometry SARs (n-pass interferometry, DInSAR; permanent scatterer interferometry (PSI); and small baseline subset (SBAS)). Two methods were chosen to evaluate and find the best technique with which to create an MDRM: The first one was to compare the signals corrected by the AIM and the vertical component of the GPS station, which showed the AIM providing 58%, 42%, and 28% of the matching with GNSS station outputs for InSAR, PSI, and SBAS, respectively. Secondly, similarity measures and Davies–Bouldin index scores were implemented to find an accurate and reliable interval in which the SBAS technique with the unsupervised K-medians method has been chosen. Based on GNSS vertical deformation in a 500 m radius around the station, the SBAS K-medians technique expressed up to 5.5% better deformation patterns than the map of SAR processing techniques.

Spatiotemporal Visualisation of PS InSAR Generated Space–Time Series Describing Large Areal Land Deformations Using Diagram Map with Spiral Graph

The space–time series carry information on temporal and spatial patterns in observed phenomena. The reported research integrates computational, visual and cartographic methods to support visual analysis of space–time series describing terrain surface movement. The proposed methodology for space–time series visualisation can support their analysts in investigating space–time patterns using transformation, clustering, filtration and visualisation. The presented approach involves spiral graphs for representation time dimension and cartographic visualisation through proportional point symbol map for representation of spatial dimension. The result is an intuitive visualisation of space–time series, conveying the sought-after spatio-temporal information. For practical tests, we used space–time series obtained by permanent scatterers interferometry (PS InSAR) to monitor the Earth’s surface movement above the underground gas storage (UGS) Tvrdonice, the Czech Republic. An UGS is characterised by periodic injection and withdrawal of natural gas, which induces periodic movement of the terrain above it. We have verified that our visualisation method provides the required pattern information and is easy to use.

Monitoring of large and inconsistent deformation in coal mining area using point-like target offset tracking

ABSTRACT Ground deformation in the coal mining area of Northwestern China is usually featured by large and inconsistent subsidence. Large subsidence brings great challenge to Differential Interferometric Synthetic Aperture Radar (DInSAR) and Permanent Scatterer Interferometry (PSI) technology due to the pixel-level offset in the range direction after co-registration, which could directly result in complete coherence loss even for the artificial Corner Reflectors (CRs). Offset tracking provides an alternative to capture the large deformation although the results could be biased due to the large deformation gradient and use of fixed correlation window size. In this paper, the point-like target (PT) offset tracking method is introduced to deformation monitoring in coal mining area with a levelly decreasing correlation window size. For each PT-centered SLC patch in the reference SAR image, the estimate from the larger correlation window will serve as a guide for the smaller one to locate the matching SLC patch in the slave SAR image, thus providing a guarantee that the residual offsets to be estimated remain to be a small fraction of the decreasing patch size. The offset estimates were evaluated by ground surveying results at 34 CRs obtained using single reference real-time kinematic (RTK) GPS technique, with a RMS error of 1/13-1/14 SLC pixel in both the range and azimuth directions.

Monitoring of the ground deformation in Penang Island, Malaysia using Permanent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) and Ground-Penetrating Radar (GPR) techniques

round deformations (e.g., landslide and subsidence) have substantially risen recently around Penang Island, Malaysia. The development of hillslopes for rapid urbanisation and heavy rainfall has detrimental impacts on Penang Island soil layers. Modelling, simulation, and the development of susceptibility maps were past methods used to predict potential ground deformations of Penang Island. There is no published work on the Interferometry Synthetic Aperture Radar (InSAR) technique, where ground deformation of the region has been holistically studied. This work integrates the Persistent Scatterer InSAR (PS-InSAR) technique with Ground Penetrating Radar (GPR) to monitor the deformation on the island and identify possible subsurface causes for the disturbance. Twenty-four descending Sentinel-1A datasets acquired between 14th July 2017 and 13th October 2018 were processed to monitor and map ground deformation areas with a focus on three selected regions- Batu Feringghi (BF), Paya Terubong (PT), and Tanjung Bungah (TB), where multiple landslides occurred between 2017 and 2018. InSAR results were later analysed using the Statistical Package for the Social Sciences (SPSS) tool and validated by the available Global Positioning System (GPS) analysis. InSAR analyses reveal the mean deformation values of -3.13 mm/yr, -2.76 mm/yr and -4.77 mm/yr for the BF, PT, and TB areas. GPR surveys were conducted with a 300/800 MHz dual-frequency antenna at the three selected study areas. Anomalies (wall cracks, road fissures, cavities, drains, and pipes) detected using GPR profiles correlated well with the permanent scatterer points calculated using the InSAR technique.

Land Subsidence Monitoring in Cepu Block Area Using PS-Insar Technique

Land subsidence can be caused by many factors, from natural through anthropogenic processes. Land subsidence due to anthropogenic factors results from such as fluid withdrawal, like oil and gas extraction. Cepu block is one of the oil and gas extraction areas that become Indonesia’s largest oil producer. This paper employs the Permanent Scatterers Interferometry Synthetic Aperture Radar (PS-InSAR) with C-Band Sentinel-1A imagery data to monitor the land subsidence phenomena. The PS-InSAR method is chosen because it can measure small movements in an area over time with millimeters DEM accuracy. The processing result showed deformation in the form of land subsidence in almost all of the blocks, except for Block D, with the mean velocity ranging from -27,75 to -9,78 mm/yr. An uplift phenomenon is also achieved in the eastern part of Block E. These PS-InSAR results are in the form of Line Of Sight (LOS). This paper correlates the land subsidence with geological maps to better understand the phenomena’ causes.

Fault motion reversals predating the M w 6.3 2009 L'Aquila earthquake: insights from synthetic aperture radar data

The millimetre accuracy of Synthetic Aperture Radar (SAR) measurements and related multi-temporal data analyses provide fundamental information on surface displacements caused by strong earthquakes. The multi-temporal analysis of SAR interferometry data allows for the geometry, kinematics and temporal behaviour of earthquake-generating faults to be better constrained, and is being acknowledged as a promising technique in the field of earthquake precursors. We used SAR data obtained by multi-temporal interferometric techniques such as Permanent Scatterers (PS) interferometry for the investigation of pre- to post-seismic ground displacements in the region struck by the M w 6.3, 2009 L'Aquila earthquake. We analysed Europen Remote Sensing (ERS) and Envisat PS-datasets from ascending and descending orbits, and COSMO-SkyMed PS-datasets from descending orbit, collectively covering a > 20 year long time span. On a yearly scale, a reversal of motions that affected the hanging-wall and footwall blocks of the earthquake-generating fault is detected. In particular, the hanging-wall block is characterized by pre-seismic uplift – which we document as being independent of any hydrological control – and eastward horizontal motion for about six years, followed by subsidence and westward motion (starting six to eight months prior to the earthquake). We suggest that such a ground displacement pattern may represent an earthquake precursor signal. Supplementary material: Figures showing SAR satellites viewing geometry, examples of raw PS time series of obtained from ERS ascending and descending orbits and maps of LoS-oriented mean velocities obtained from PS datasets from the Envisat ascending and descending orbits and a Table reporting detailed numerical results of the performed statistical analysis are available at https://doi.org/10.6084/m9.figshare.c.5289357

Multi-Sensor InSAR Assessment of Ground Deformations around Lake Mead and Its Relation to Water Level Changes

Changes in subsurface water resources might alter the surrounding ground by generating subsidence or uplift, depending on geological and hydrogeological site characteristics. Improved understanding of the relationships between surface water storage and ground deformation is important for design and maintenance of hydraulic facilities and ground stability. Here, we construct one of the longest series of Interferometric Synthetic Aperture Radar (InSAR) to date, over twenty-five years, to study the relationships between water level changes and ground surface deformation in the surroundings of Lake Mead, United States, and at the site of the Hoover Dam. We use the Small Baseline Subset (SBAS) and Permanent scatterer interferometry (PSI) techniques over 177 SAR data, encompassing different SAR sensors including ERS1/2, Envisat, ALOS (PALSAR), and Sentinel-1(S1). We perform a cross-sensor examination of the relationship between water level changes and ground displacement. We found a negative relationship between water level change and ground deformation around the reservoir that was consistent across all sensors. The negative relationship was evident from the long-term changes in water level and deformation occurring from 1995 to 2014, and also from the intra-annual oscillations of the later period, 2014 to 2019, both around the reservoir and at the dam. These results suggest an elastic response of the ground surface to changes in water storage in the reservoir, both at the dam site and around the reservoir. Our study illustrates how InSAR-derived ground deformations can be consistent in time across sensors, showing the potential of detecting longer time-series of ground deformation.

Time Series Deformation Monitoring over Large Infrastructures around Dongting Lake Using X‐Band PSI with a Combined Thermal Expansion and Seasonal Model

The long‐term spatial‐temporal deformation monitoring of densely distributed infrastructures near the lake area is of great significance to understand the urban health status and prevent the potential traffic safety problems. In this paper, the permanent scatterer interferometry (PSI) technology with TerraSAR‐X imagery over the area around Dongting Lake was utilized to generate the long‐term spatial‐temporal deformation. Since the X‐band SAR interferometric phases are highly influenced by the thermal dilation of the observed objects, and the deformation of large infrastructures are highly related to external temperature, a combined deformation model considering the thermal expansion and the seasonal environmental factors was proposed to model the temporal variations of the deformation. The time series deformation and the thermal dilation parameter over the area were obtained, and a comparative study with the traditional linear model was conducted. The Dongting Lake Bridge and the typical feature points distributed around the lake were analyzed in details. In order to compensate for the unavailability of external in situ measurements over the area, phase residuals and the subsidence generated through Differential Interferometric Synthetic Aperture Radar (D‐InSAR) were utilized to verify the accuracy of the obtained deformation time series. Experiment results suggested that the proposed model is suitable and suggested for the selected study site. The root mean square error (RMSE) of the residual phase was estimated as 0.32 rad, and the RMSE compared with D‐InSAR derived deformation was ±1.1 mm.

Detection and 3D Visualization of Deformations for High-Rise Buildings in Shenzhen, China from High-Resolution TerraSAR-X Datasets

Shenzhen, a coastal city, has changed from a small village to a supercity since the late 1980s. With the rapid development of its population and economy, ground disasters also occur frequently. These disasters bring great harm to human life and surface architecture. However, there is a lack of regular ground measurement data in this area. Permanent scatterer interferometry (PSI) technology can detect millimeter deformation of urban surface. In this paper, the building height and deformation from 2008 to 2010 in the Futian District of Shenzhen are obtained by using this technique alongside high-resolution TerraSAR-X data. For a visual expression of the result, we export the permanent scatterer (PS) points on buildings to Google Earth for 3D visualization after ortho-rectification of the PS height. Based on the Google Earth 3D model, the temporal and spatial characteristics of the building deformation became obvious. The InSAR measurements show that during the study period, the deformation rates of the Futian area are between −10 and 10 mm/year, and deformation is mainly distributed in a few buildings. These unstable activities can be attributed to human activities and the natural climate, which provides a reference for the local government to carry out a survey of surface deformation, as well as the monitoring and management of urban buildings, in the future.

A Model for Complex Subsidence Causality Interpretation Based on PS-InSAR Cross-Heading Orbits Analysis

Urban areas are subject to subsidence due to varying natural and anthropogenic causes. Often, subsidence is interpreted and correlated to a single causal factor; however, subsidence is usually more complex. In this study, we adopt a new model to distinguish different causes of subsidence in urban areas based on complexity. Ascending and descending Sentinel-1 data were analyzed using permanent scatterer interferometry (PS-InSAR) and decomposed to estimate vertical velocity. The estimated velocity is correlated to potential causes of subsidence, and modeled using different weights, to extract the model with the highest correlations among subsidence. The model was tested in Alexandria City, Egypt, based on three potential causes of subsidence: rock type, former lakes and lagoons dewatering (FLLD), and built-up load (BL). Results of experiments on the tested area reveal singular patterns of causal factors of subsidence distributed across the northeast, northwest, central south, and parts of the city center, reflecting the rock type of those areas. Dual causes of subsidence are found in the southwest and some parts of the southeast as a contribution of rock type and FLLD, whereas the most complex causes of subsidence are found in the southeast of the city, as the newly built-up areas interact with the rock type and FLLD to form a complex subsidence regime. Those areas also show the highest subsidence values among all other parts of the city. The accuracy of the final model was confirmed using linear regression analysis, with an R2 value of 0.88.

Application of particle swarm optimization to the estimation of the TSInSAR deformation parameter

ABSTRACTTime-series Interferometric Synthetic Aperture Radar (TSInSAR) is a significant technology that monitors the earth’s surface deformation. Obtaining a highly accurate deformation parameter is challenging because of errors caused by local extremums. In this letter, we introduce Particle Swarm Optimization (PSO) to solve this problem. The deformation rate and elevation correction parameters to be estimated can be regarded as a particle in continuous two-dimensional space. An iterative search strategy is executed so that the particles move in the solution space. Cooperation and information sharing between particles are utilized in the search strategy, which is expected to move the particles toward the best solution and avoid the local optimum. To validate the reliability of PSO, 23 Synthetic Aperture Radar (SAR) images in Beijing were exploited to retrieve deformation by PSO as well as by standard Permanent Scatterer Interferometry (PSI). We compared the results with the corresponding levelling data. Experiment suggested that there about 11% points have higher accuracy by employing PSO than those by standard PSI.

Sentinel-1 for Monitoring Land Subsidence of Coastal Cities in Africa Using PSInSAR: A Methodology Based on the Integration of SNAP and StaMPS

The sub-Saharan African coast is experiencing fast-growing urbanization, particularly around major cities. This threatens the equilibrium of the socio-ecosystems where they are located and on which they depend: underground water resources are exploited with a disregard for sustainability; land is reclaimed from wetlands or lagoons; built-up areas, both formal and informal, grow without adequate urban planning. Together, all these forces can result in land surface deformation, subsidence or even uplift, which can increase risk within these already fragile socio-ecosystems. In particular, in the case of land subsidence, the risk of urban flooding can increase significantly, also considering the contribution of sea level rise driven by climate change. Monitoring such fast-changing environments is crucial to be able to identify key risks and plan adaptation responses to mitigate current and future flood risks. Persistent scatterer interferometry (PSI) with synthetic aperture radar (SAR) is a powerful tool to monitor land deformation with high precision using relatively low-cost technology, also thanks to the open access data of Sentinel-1, which provides global observations every 6 days at 20-m ground resolution. In this paper, we demonstrate how it is possible to monitor land subsidence in urban coastal areas by means of permanent scatterer interferometry and Sentinel-1, exploiting an automatic procedure based on an integration of the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatterers (StaMPS). We present the results of PSI analysis over the cities of Banjul (the Gambia) and Lagos (Nigeria) showing a comparison of results obtained with TerraSAR-X, Constellation of Small Satellites for the Mediterranean Basin Observation (COSMO-SkyMed) and Environmental Satellite advanced synthetic aperture radar (Envisat-ASAR) data. The methodology allows us to highlight areas of high land deformation, information that is useful for urban development, disaster risk management and climate adaptation planning.

Damage Detection and Analysis of Urban Bridges Using Terrestrial Laser Scanning (TLS), Ground-Based Microwave Interferometry, and Permanent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR)

This paper presents a practical framework for urban bridge damage detection and analysis by using three key techniques: terrestrial laser scanning (TLS), ground-based microwave interferometry, and permanent scatterer interferometry synthetic aperture radar (PS-InSAR). The proposed framework was tested on the Beishatan Bridge in Beijing, China. Firstly, a Digital Surface Model (DSM) of the lower surface of the bridge was constructed based on the point cloud generated by using TLS to obtain the potential damage area. Secondly, the dynamic time-series displacement of the potential damage area was acquired by ground-based microwave interferometry, and the Extreme-Point Symmetric Mode Decomposition (ESMD) method was applied to detect damages by the use of signal decomposition and instantaneous frequency calculation. Lastly, the PS-InSAR technique was applied to obtain the surface deformation around Beishatan Bridge by using COSMO-SkyMed images with a ground resolution of 3 m × 3 m, and finally, we analyzed the causes of bridge damage. The experimental results showed that the proposed framework can effectively obtain the potential damage area of the bridge by the DSM from the point cloud by TLS and further judge whether the bridge was damaged by the ESMD method, based on the time-series displacement data. The results also showed that the subway shield construction may be the reason for damage to Beishatan Bridge.

A new maximum likelihood polarimetric interferometric synthetic aperture radar coherence change detection (ML-PolInSAR-CCD)

ABSTRACTCoherent change detection (CCD) use the sample degree of coherence as a measure of the temporal change collected between two complex-valued SAR images observed in the same area and using also the same geometry and polarization. The problem of classical CCD approaches shows a temporal change only when there are in areas of the image that have high clutter-to-noise power ratio. All experiments regarding the maximum-likelihood (ML) CCD, found to estimate useful information also in lower clutter-to-noise power ratio. Experiments used only electromagnetic transmissions in the same polarization. This work extends the formulation of the probability distribution function of the CCD in a multi-dimensional version useful for multi-polarization SAR data. Results of this ML-polarimetric interferometric SAR-CCD (ML-PolInSAR-CCD) show surprising recovery of both amplitude and phase CCD information. This information recovery is useful for improved interferometric SAR (InSAR), permanent scatterers interferometry (PS-InSAR) and SAR tomography applications.