Permanent Scatterers Research Articles

An Improved Method for Phase Triangulation Algorithm Based on the Coherence Matrix Eigen-Decomposition in Time-Series SAR Interferometry

Time-series SAR interferometry, which combines permanent scatterers (PSs) and distributed scatterers (DSs), has been strongly developed in recent years. Unlike PS, DS corresponds to a natural target whose neighboring pixels share similar reflectivity values. The selection of DS is relevant to the goodness-of-fit value, the estimation of which is based on all possible combined interferometric phases and fails to avoid the adverse effect of low-quality phases. This paper used eigen-decomposition of coherence matrix that was constructed based on the identified homogeneous pixels to perform phase optimization, and then only the interferometric phases with low noise and clear fringes are utilized to measure the goodness-of-fit. 30 Sentinel-1A images were applied to test the improved method of land subsidence monitoring in Beijing, China. The deformation results of different methods were cross verified and the area statistics of the study area were carried out. The results show that the maximum subsidence monitored by the improved method is located in Jinzhan Town with a maximum rate of −109 mm/yr. This improved method extracts more measurement points with accuracy ensured, which is proved to be an effective way to provide the high spatial density of deformation measurements. The land subsidence in this area is mainly caused by the excessive exploitation of groundwater resources. This research provides support for the prevention and control work of relevant departments.

Mining Subsidence Monitoring With Modified Time-Series SAR Interferometry Method Based on the Multi-Level Processing Strategy

The supply of coal is related to the stability of social development in China. Coal mining faces are generally located under farmland and wasteland to avoid destroying the livelihood and property of residents, which seriously affects the selection of permanent scatterer (PS) with time-series interferometric synthetic aperture radar (TS-InSAR) method. The advanced TS-InSAR (ATS-InSAR) method combining PS and distributed scatterer (DS) monitoring modules is a useful tool to increase measurement points. However, the number of DS and the accuracy of measurement are different with different thresholds of temporal coherence. On the basis of ATS-InSAR, we propose a modified method applying the multi-level processing strategy to obtain more reliable deformation information in this paper. 16 sentinel-1A images are used to monitor mining subsidence in Peixian, China. The results of three different methods are cross-verified. Meanwhile, reliable DS pixels are identified by using the thresholding of both temporal coherence and Pearson correlation coefficient through the hierarchical processing. The results show that the deformation of the modified method reveals a large subsidence with the maximum rate of -563 mm/yr. The number of measurement points selected by the modified method is about 6.6 times that of the TS-InSAR method, and 1.3 times that of the ATS-InSAR method. The modified strategy can extract a great number of reliable pixels and reduce error propagation to ensure measurement accuracy. This research offers information to relevant departments for risk management purpose.

Using TerraSAR X-Band and Sentinel-1 C-Band SAR Interferometry for Deformation Along Beijing-Tianjin Intercity Railway Analysis

The Beijing-Tianjin Intercity Railway (BTIR) is an important transportation hub connecting Beijing and Tianjin. It is the China's first high-speed railway reaching a speed of 350 km/h and has been in operation for ten years. Since the foundation of the BTIR is made up of soft and loose soil, hence suffers greatly from potential deformation. In order to investigate the impact of ground subsidence on the railway line, 26 TerraSAR-X SpotLight images, 23 TerraSAR-X StripMap images, and 31 Sentinel-1 Interferometric Wide images were processed to obtain the deformation of the Yizhuang areas along the BTIR from January 2015 to March 2017 by permanent scatterers interferometric synthetic aperture radar technology. The deformation results of these three datasets are self-consistent. Through the vertical and horizontal deformation of the railway using fusion of the railway deformation results from different line of sight direction, we found that the deformation of the railway line is mainly in the vertical direction. Furthermore, the high precision spatial-temporal evolution of the deformation along the BTIR is revealed. Finally, the advantages and limitations of different SAR resolution are analyzed and summarized.

A study of land subsidence in west of Tehran using Sentinel-1 data and permanent scatterer interferometric technique

In recent years, subsidence is considered one of the major environmental challenges in semiarid and arid regions of Iran. The main reason of land subsidence in Iran is injudicious groundwater extraction for agricultural and industrial activities. This study focuses on monitoring the displacement level of the ground in the west of Tehran which is highly affected by subsidence of Shahriar and Tehran plains. To support this study, Sentinel-1 images in both ascending (28) and descending (35) paths were used. The maximum subsidence rates of 16 cm/year and 15 cm/year were estimated in the southwest of Tehran and Shahriar Plain, respectively. Three subsidence centers were discovered in Shahriar Plain which is located in an urban area. The maximum LOS velocity was calculated about 5 cm/year in Fath Highway. The maximum rate of subsidence was estimated approximately 20-70 mm/year in the vicinity of Qods City. In Shahriar Plain, accuracy of InSAR results was assessed using cross-comparison analysis of the ascending and descending paths. However, in the southwest of Tehran, the vertical velocity obtained from Sentinel-1 data is validated using the GPS observations. The validations confirmed the accuracy of the PSInSAR algorithm and the InSAR results.

Monitoring Bridge Vibrations Based on GBSAR and Validation by High-Rate GPS Measurements

Ground-Based synthetic aperture radar (GBSAR) is a novel technique for monitoring surface deformation and structural vibrations due to its unique advantages: GBSAR can continually and simultaneously observe a large range of landscapes with a high spatiotemporal resolution. Using GBSAR under the fixed azimuth scanning mode to monitor vibrations is a promising way to monitor a bridge's structural health. Several GBSAR-based bridge vibration experiments have been conducted and verified; however, observation precision tests using high-rate global positioning system (GPS) data are rare, especially for the GAMMA portable radar interferometer II (GPRI-II). In addition, it is complicated to split high-frequency signals into valuable information and undesired noise. Considering these limitations, an on-site experiment using the GPRI-II and high-rate GPS receivers was conducted to evaluate the utility of the GPRI-II. Moreover, a denoising method based on the least-squares adjustment and a triangular chain is proposed under the assumption that the atmospheric phase delay can be eliminated between minuscule time intervals. In addition, an external comparative experiment between the GPRI-II and GPS receivers and an internal comparative experiment was performed to evaluate the effectiveness and reliability, respectively, of the proposed method. The results show that the GPRI-II observation accuracy for permanent scatterers reaches 5-6 millimeters. For the effectiveness of the proposed method, the precision improvement for distributed scatterers is more significant than that for permanent scatterers, and both the internal precision and the external precision have more conspicuous relationships with the deviation of the amplitude than with the coherence.

Quantitative determination of the optimal threshold of Permanent Scatterer based on ROC Curve

Quantitative determination of the optimal threshold of Permanent Scatterer based on ROC Curve

Automatic Detection of Volcanic Unrest Using Blind Source Separation With a Minimum Spanning Tree Based Stability Analysis

Repeated synthetic aperture radar (SAR) acquisitions can be utilized to produce measurements of ground deformations and associated geohazards, such as it can be used to detect signs of volcanic unrest. Existing time series algorithms like permanent scatterer analysis and small baseline subset are computationally demanding and cannot be applied in near real time to detect subtle, transient, and precursory deformations. To overcome this problem, we have adapted a minimum spanning tree based spatial independent component analysis method to automatically detect sources related to volcanic unrest from a time series of differential interferograms. For a synthetic dataset, we first utilize the algorithm's capability to isolate signals of geophysical interest from atmospheric artifacts, topography, and other noise signals, before monitoring the evolution of these signals through time in order to detect the onset of a period of volcanic unrest, in near real time. In this article, we first demonstrate our approach on synthetic datasets having different signal strengths and temporal complexities. Second, we demonstrate our approach on a couple of real datasets, one acquired in 2017–2019 over the Colima volcano, Mexico, showing the occurrence of previously unrecognized short-term deformation events and the other over Mt. Thorbjorn in Iceland acquired over 2020. This shows the strength of the deep learning application to differential interferometric SAR measurements, and highlights that deformation events occurring without eruptions, which may have previously been undetected.

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.

Deep Learning for RFI Artifact Recognition in Sentinel-1 Data

Beyond the variety of unwanted disruptions that appear quite frequently in synthetic aperture radar (SAR) measurements, radio-frequency interference (RFI) is one of the most challenging issues due to its various forms and sources. Unfortunately, over the years, this problem has grown worse. RFI artifacts not only hinder processing of SAR data, but also play a significant role when it comes to the quality, reliability, and accuracy of the final outcomes. To address this issue, a robust, effective, and—importantly—easy-to-implement method for identifying RFI-affected images was developed. The main aim of the proposed solution is the support of the automatic permanent scatters in SAR (PSInSAR) processing workflow through the exclusion of contaminated SAR data that could lead to misinterpretation of the calculation results. The approach presented in this paper for the purpose of recognition of these specific artifacts is based on deep learning. Considering different levels of image damage, we used three variants of a LeNet-type convolutional neural network. The results show the high efficiency of our model used directly on sample data.

Land Subsidence Detection in Tan My-Thuong Tan Open Pit Mine and Surrounding Areas by Time Series of Sentinel-1 Images

Open-pit or underground mining both causes environmental impacts such as air, soil, water pollution, etc., especially causing land subsidence of mines and surrounding areas. Research on mining subsidence is often carried out by field survey, the advantage of this method is high accuracy, but it is usually applied in a small scale. Recently, with the development of radar technology, there have been many studies applying this Radar Interferometry technique to determine surface subsidence over a wide range with a few millimeters accuracy. In this paper, 24 Sentinel-1 images were used as input materials, using the Permanent Scatter Interferometry (PSInSAR) method to determine the land subsidence of the Tan My-Thuong Tan quarries and surrounding areas. The results were compared with the average annual subsidence of 20 surveying points using GNSS technology from 1/2018 to 3/2020. The correlation coefficient of annual average land subsidence of the two methods is 0.83, showing the feasibility of applying InSAR Sentinel-1 data processed by PSInSAR method for determining mine surface deformation and surrounding area.

On the Assessment of Non-Local Multi-Looking in Detection of Persistent Scatterers Using SAR Tomography

Synthetic aperture radar (SAR) tomography has shown great potential in multi-dimensional monitoring of urban infrastructures and detection of their possible slow deformations. Along this line, undeniable improvements in SAR tomography (TomoSAR) detection framework of multiple permanent scatterers (PSs) have been observed by the use of a multi-looking operation that is the necessity for data’s covariance matrix estimation. This paper attempts to further analyze the impact of a robust multi-looking operation in TomoSAR PS detection framework and assess the challenging issues that exist in the estimation of the covariance matrix of large stack data obtained from long interferometric time series acquisition. The analyses evaluate the performance of non-local covariance matrix estimation approaches in PS detection framework using the super-resolution multi-looked Generalized Likelihood Ratio Test (GLRT). Experimental results of multi-looking impact assessment are provided using two datasets acquired by COSMO-SkyMED (CSK) and TerraSAR-X (TSX) over Tehran, Iran, and Toulouse, France, respectively. The results highlight that non-local estimation of the sample covariance matrix allows revealing the presence of the scatterers, that may not be detectable using the conventional local-based framework.

A NEW METHOD TO EXTRACT PERMANENT SCATTERERS

Abstract. At present, time series InSAR technology has been widely used in surface deformation monitoring. The extraction of permanent scatterers is an important part, which is directly related to the accuracy of monitoring results. The existing permanent scatterers extraction methods are mainly based on the amplitude information or the coherence information, there is a problem that the point quality and point density cannot be taken into account, and the selection of the point parameters requires operators to have a wealth of experience. In order to solve the above problems, a permanent scatterers extraction method based on the combination of amplitude and model coherence coefficient is proposed in this paper. This method examines not only the amplitude information of the permanent scatterers, but also the phase information of the point. And the phase quality directly affects the accuracy of deformation inversion. This paper takes Yupu Bridge, Yiqiao Town, Xiaoshan District, Hangzhou City as the experimental area to carry out comparative experiments. The results show that the final point target density extracted by this method is 1.97 times that of the conventional method based on amplitude information, and shows the details of deformation distribution of Yupu Bridge more completely.

Characterisation of Landslide using InSAR Technique and Electrical Resistivity Method at Paya Terubong, Malaysia

Ground movement is common around hilly areas; as such, there is a need to investigate the activities of the ground surface and subsurface of such regions. This research presents an application of the Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique and electrical resistivity (ER) method for the measurement of ground deformations and investigation of the subsurface features, respectively, at Paya Terubong, Penang Island. The PS-InSAR is an instrument for detecting patterns of deformation over a wide region, while the ER approach is used to image subsurface characteristics and measure the resistivity of the rock formation. Satellite data obtained from Sentinel-1A (S-1A) were used to detect some active ground deformation zones. The velocity of − 41 mm/Yr was discovered along the line of sight (LOS) of the system, indicating subsidence. An ER survey was performed at the topsoil within 5 m depth based on the ground deformation patterns detected through the InSAR technique. Two-dimensional (2-D) resistivity lines were laid using the ABEM SAS4000 Terameter and a dipole–dipole array with an electrode spacing of 2.5 m. The areas with low values of resistivity ranging between 0 and 20 Ωm were classified as saturated zones. The efflux and influx of subsurface water when the layers of rocks are lubricated and downcutting of steep slopes are among the suspected causes of deformation. The application of the two techniques has improved the understanding of both the surface and subsurface mechanisms, reduced variation in the results, and allowed the investigation of active landslide areas.

Dynamic susceptibility mapping of slow-moving landslides using PSInSAR

ABSTRACT A landslide susceptibility map (LSM) is a valuable tool for landslide assessment and land use management. This research proposes a landslide susceptibility dynamic map (DLSM) to increase LSM utility and update the predicted map in a time series. Slope units, as basic mapping units, were produced to define the landslide boundaries and simplify the mapping in the DLSM. The permanent scatterer interferometric synthetic aperture radar (PSInSAR) technique was used to estimate the line of sight velocity (V los). This was then reprojected into the velocity in the steepest slope direction (V slope) to avoid the influence of geometric distortion. The DLSM was produced by integrating, using slope unit aggregate values, the susceptibility (probability) of landsliding predicted by logistic regression based on eight spatial covariates and the V slope predicted using the PSInSAR technique. The DLSM is a dynamically changing susceptibility map in which susceptibility is increased in certain months, particularly where surface velocity increases following the rainy season. The proportion of the study area classified with extremely high susceptibility increased from 22.2% to 44.8% after the rainy season. The DLSM, thus, potentially improves the prediction reliability for slow-moving landslides and, in particular, can help to avoid false negatives. The DLSM can be applied in areas for which radar data are available and can provide more reliable and readily interpretable results to decision-makers.

InSAR observations and analysis of the Medicina Geodetic Observatory and CosmoSkyMed images

We have observed some discrepancies between installed geodetic instruments including global positioning systems (GPS) and very long baseline interferometry (VLBI) in the Medicina Geodetic Observatory (MGO). This study overcomes the above-mentioned discrepancies. We analysed several CosmoSkyMed images utilizing the interferometric synthetic aperture radar (InSAR) technique to improve the understanding of 3D surface displacements. The MGO is in a rural area, and the radar coherence is low; therefore, the coverage of permanent scatterers (PS) is limited. In ascending mode, the closest scatterer to the MEDI GPS station shows that the surface displacements are as large as 0.72 ± 1.45 mm/year along the line of sight (LOS) direction. The GPS height projected on the LOS vector shows a rate of 0.92 ± 0.04 mm/year of displacements. In descending mode, the closest scatterer to MEDI shows that the displacement is at a rate of − 0.1 ± 0.51 mm/year along the LOS direction, and the MEDI projected on the LOS vector is at a rate of 0.91 ± 0.002 mm/year. In this study, the deformation rates and their standard deviations stress that the GPS and PS-InSAR have different values. Despite a large (32-m-diameter parabolic antenna) VLBI telescope, we do not observe a single PS on the telescope for ascending and descending passes due to the frequent movements of the VLBI telescope in different directions.

Land subsidence mapping and monitoring using modified persistent scatterer interferometric synthetic aperture radar in Jharia Coalfield, India

Subsidence has been adversely affecting Jharia Coalfield (JCF) for the last few decades. This study attempts to show the feasibility of the modified Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique with C-band SAR data to investigate the slow surface deformation caused by coal mine fire and underground mining activities in JCF. Also, a multi-temporal analysis of SAR images of ENVISAT ASAR has been carried out for monitoring and mapping of temporal land subsidence of the area under study. The modified PSI technique has proven its ability to detect land subsidence over the vegetated and rural areas. It also resolves low spatial density of permanent scatterers by considering partially correlated scatterers as permanent scatterers (PSs) and extracting information from these PSs. The study has been concentrated towards detecting continuous slow rate subsidence of five major sites of JCF. The maximum rate of slow deformation among all sites is recorded as 29 mm/year with a cumulative subsidence value of 90 mm. Field validation of subsidence results obtained through PS-InSAR is correlated with the previously published report and the master plan of JCF, showing subsidence locations. Conclusively, the adopted methodology is practically feasible for detection, monitoring and mapping of slow deformation using C-band SAR data in coal mine area.

RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques.

Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used.

The pattern of surface deformations in the Dąbrowa Basin (Poland) based on PSInSAR analysis

Slow, long-term ground deformations in the Dąbrowa Basin (southern Poland) were identified based on ERS-1 , ERS-2 and ENVISAT Synthetic Aperture Radar (SAR) images that were processed by means of Permanent Scatterer SAR Interferometry (PSInSAR). The Dąbrowa Basin is a region where two major factors can affect surface stability: intensive coal exploitation and neotectonic processes. In this study, in order to clarify the origin of surface deformations, the authors propose applying a newly developed algorithm of spatio-temporal PSInSAR data analysis. This analysis revealed that subsidence is a characteristic feature of the Dąbrowa Basin. A significant correlation exists between slow, long-term ground deformations and the location of the main tectonic structure of this region. The proposed spatiotemporal analysis of the PSInSAR data additionally showed some degree of correlation between mining activity and the studied deformations. This interconnection is a significant achievement of this study since the deformation values determined by means of PSInSAR were identified in earlier works solely on the basis of Dąbrowa Basin neotectonics. Normal 0 21 false false false PL X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Standardowy; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;}

A New Parameterization for the Rician Distribution

The Rician distribution is widely used in SAR imagery to model the backscattering of a strong target inside a resolution cell. Nevertheless, the computation of the parameters of the Rice distribution remains a difficult task. In this letter, a new parameterization to model the Rice distribution is introduced. Thanks to the introduction of a new variable defined by the ratio of the target contribution to the speckle, the relationship between the coefficient of variation and this new parameter can be derived. An efficient numerical method is proposed to evaluate it from the coefficient of variation and a discussion on the variance of this estimator is led. A comparison with other methods of estimation showed that the proposed approach is a good compromise between the variance of the estimate and the computation time. At last, a link between the permanent scatterers and the Rice distributed targets is proposed through this new parameterization.

Joint Exploitation of SAR and GNSS for Atmospheric Phase Screens Retrieval Aimed at Numerical Weather Prediction Model Ingestion

This paper proposes a simple and fast method to estimate Atmospheric Phase Screens (APSs) by jointly exploit a stack of Synthetic Aperture Radar (SAR) images and a dataset of GNSS-derived atmospheric product. The output of this processing is conceived to be ingested by Numerical Weather Prediction Models (NWPMs) to improve weather forecasts. In order to provide wide and dense area coverage and to respect requirements in terms of spatial resolution of ingestion products in NWPMs, both Permanent Scatterers (PSs) and Distributed Scatterers (DSs) are jointly exploited. While the formers are by definition stable targets, but unevenly distributed, the latter are ubiquitous but stable only within a certain temporal baseline that can vary depending on the operational frequency of the radar. The proposed method is thus particularly suited for C, L, and P band missions with low temporal baseline between two consecutive acquisitions of the same scene: these conditions, that are both necessary to provide the dense space-time coverage required by meteorologists, allow for a reliable and robust estimation of APSs thanks to the intrinsic limitation of temporal decorrelation. The proposed technique integrates Zenith Total Delay (ZTD) products computed on a very sparse grid from a network of GNSS stations to correct for SAR orbital errors and to provide the missing phase constant from the derived APS map. In this paper, the complete workflow is explained, and a comparison of the derived APSs is performed with phase screens derived from state-of-the-art SAR processing workflow (SqueeSAR®).