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Special Issue "Imaging Geodesy and Infrastructure Monitoring"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (1 September 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Mahdi Motagh

Department of Geodesy, GFZ German Research Center for Geosciences, Potsdam, and Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, Hannover, Germany
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Interests: radar remote sensing for geoscience and engineering applications, multitemporal InSAR time-series techniques, geophysical and numerical modeling of deformation processes
Guest Editor
Prof. Dr. Richard Bamler

German Aerospace Center (DLR), Earth Observation Center (EOC), Remote Sensing Technology Institute, Oberpfaffenhofen, 82234 Weßling, and Technical University of Munich, Germany
Website | E-Mail
Interests: information retrieval from remote sensing data; inversion and estimation theory; sparse signal reconstruction and compressive sensing; algorithms for processing sensor data with focus on synthetic aperture radar (SAR); SAR interferometry and tomography
Guest Editor
Prof. Dr. Zhenhong Li

School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Website | E-Mail
Phone: +44 (0) 191 208 5704
Interests: synthetic aperture radar (SAR); interferometric SAR (InSAR); time series; precision agriculture; GNSS
Guest Editor
Prof. Dr. Ramon Hanssen

Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands
Website | E-Mail
Interests: geodetic analysis of imaging remote sensing data, the influence of the atmosphere on space-geodetic techniques, mathematical modeling and physical interpretation of deformation processes

Special Issue Information

Dear Colleagues,

The surge in the availability of high spatial resolution Synthetic Aperture Radar (SAR) data has allowed ever increasing use of modern SAR sensors for mapping applications and investigating deformation processes related to natural and man-made hazards. Significant improvements in interferometric, polarimetric, and tomographic processing, coupled with development of high-resolution SAR sensors aboard missions such as Italian COSMO-SkyMed (CSK) and German TerraSAR-X (TSX), have created new opportunities for detailed imaging of buildings and analyzing motion and thermal changes related to infrastructure in urban environment. Very high-resolution interferometric methods are one side of the opportunities, the other is the dramatic increase of absolute geometric localization accuracy of these methods which can reach the cm-regime. However, the full potential of space-borne and ground-based SAR technologies within civil and surveying engineering community is still unrecognized.

The main objective of this special issue is to present the progress, and state-of-the-approaches in algorithm development and scientific exploitation of SAR data to retrieve information about infrastructure. Contributions reporting on SAR tomography and compressive sensing, combination of SAR/InSAR data with other optical and geotechnical sensors for urban mapping and improving the efficiency of remote sensing products for operational monitoring, integration of SAR/InSAR products with numerical and analytical geotechnical models for stability analysis of infrastructure, polarimetric analysis of urban environment as well as contributions to the achievement and use of cm-level absolute geolocation accuracy are welcome.

Prof. Dr. Mahdi Motagh
Prof. Dr. Richard Bamler
Prof. Dr. Zhenhong Li
Prof. Dr. Ramon Hanssen
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Methodologies:

  • SAR/InSAR processing
  • SAR Tomography
  • SqueeSAR, SBAS and other algorithms for exploiting distributed scatterers
  • SAR Polarimetry
  • Stereo and correlation methods
  • Geodetic accuracy
  • Geodetic corrections (atmosphere, geodynamics, etc.)
  • Super-resolution for SAR infrastructure imaging
  • Fusion of InSAR data and other sources for infrastructure monitoring
  • Machine learning for InSAR
  • Validation methods

Application fields:

  • Mining activities
  • Infrastructure monitoring
  • Sea and river level gauges
  • Landslides

Published Papers (11 papers)

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Research

Open AccessArticle
Detection of Building and Infrastructure Instabilities by Automatic Spatiotemporal Analysis of Satellite SAR Interferometry Measurements
Remote Sens. 2018, 10(11), 1816; https://doi.org/10.3390/rs10111816
Received: 14 September 2018 / Revised: 1 November 2018 / Accepted: 6 November 2018 / Published: 16 November 2018
Cited by 1 | PDF Full-text (14803 KB) | HTML Full-text | XML Full-text
Abstract
Satellite synthetic aperture radar (SAR) interferometry (InSAR) is a powerful technology to monitor slow ground surface movements. However, the extraction and interpretation of information from big sets of InSAR measurements is a complex and demanding task. In this paper, a new method is [...] Read more.
Satellite synthetic aperture radar (SAR) interferometry (InSAR) is a powerful technology to monitor slow ground surface movements. However, the extraction and interpretation of information from big sets of InSAR measurements is a complex and demanding task. In this paper, a new method is presented for automatically detecting potential instability risks affecting buildings and infrastructures, by searching for anomalies in the persistent scatterer (PS) deformations, either in the spatial or in the temporal dimensions. In the spatial dimension, in order to reduce the dataset size and improve data reliability, we utilize a hierarchical clustering method to obtain convergence points that are more trustworthy. Then, we detect deformations characterized by large values and spatial inhomogeneity. In the temporal dimension, we use a signal processing method to decompose the input into two main components: regular periodic deformations and piecewise linear deformations. After removing the periodic component, the velocity variation in each identified temporal partition is analyzed to detect anomalous velocity trends and accelerations. The method has been tested on different sites in China, based on InSAR measurements from COSMO-SkyMed data. The results, verified with in-field surveys, confirm the potential of the method for the automatic detection of deformation anomalies that could cause building or infrastructure stability problems. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Open AccessArticle
Monitoring Coastal Reclamation Subsidence in Hong Kong with Distributed Scatterer Interferometry
Remote Sens. 2018, 10(11), 1738; https://doi.org/10.3390/rs10111738
Received: 30 August 2018 / Revised: 29 October 2018 / Accepted: 1 November 2018 / Published: 3 November 2018
PDF Full-text (11936 KB) | HTML Full-text | XML Full-text
Abstract
Land subsidence has been a significant problem in land reclaimed from the sea, and it is usually characterized by a differential settlement pattern due to locally unconsolidated marine sediments and fill materials. Time series Synthetic Aperture Radar Interferometry (InSAR) techniques based on distributed [...] Read more.
Land subsidence has been a significant problem in land reclaimed from the sea, and it is usually characterized by a differential settlement pattern due to locally unconsolidated marine sediments and fill materials. Time series Synthetic Aperture Radar Interferometry (InSAR) techniques based on distributed scatterers (DS), which can identify sufficient measurement points (MPs) when point-wise radar targets are lacking, have great potential to measure such differential reclamation settlement. However, the computational time cost has been the main drawback of current distributed scatterer interferometry (DSI) for its applications compared to the standard PSI analysis. In this paper, we adopted an improved DSI processing strategy for a fast and robust analysis of land subsidence in reclaimed regions, which is characterized by an integration of fast statistically homogeneous pixel selection based (FaSHPS-based) DS detection and eigendecomposition phase optimization. We demonstrate the advantages of the proposed DSI strategy in computational efficiency and deformation estimation reliability by applying it to two TerraSAR-X image data stacks from 2008 to 2009 to retrieve land subsidence over two typical reclaimed regions of Hong Kong International Airport (HKIA) and Hong Kong Science Park (HKSP). Compared with the state-of-the-art DSI methods, the proposed strategy significantly improves the computational efficiency, which is enhanced approximately 30 times in DS identification and 20 times in phase optimization. On average, the DSI strategy results in 7.8 and 3.7 times the detected number of MPs for HKIA and HKSP with respect to persistent scatter interferometry (PSI), which enables a very detailed characterization of locally differential settlement patterns. Moreover, the DSI-derived results agree well with the levelling survey measurements at HKIA, with a mean difference of 1.87 mm/yr and a standard deviation of 2.08 mm/yr. The results demonstrate that the proposed DSI strategy is effective at improving target density, accuracy and efficiency in monitoring ground deformation, particularly over reclaimed coastal areas. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Open AccessArticle
Hazard Implications of the 2016 Mw 5.0 Cushing, OK Earthquake from a Joint Analysis of Damage and InSAR Data
Remote Sens. 2018, 10(11), 1715; https://doi.org/10.3390/rs10111715
Received: 6 September 2018 / Revised: 21 October 2018 / Accepted: 21 October 2018 / Published: 30 October 2018
Cited by 1 | PDF Full-text (76893 KB) | HTML Full-text | XML Full-text
Abstract
The Cushing Hub in Oklahoma, one of the largest oil storage facilities in the world, is federally designated as critical national infrastructure. In 2014, the formerly aseismic city of Cushing experienced a Mw 4.0 and 4.3 induced earthquake sequence due to wastewater injection. [...] Read more.
The Cushing Hub in Oklahoma, one of the largest oil storage facilities in the world, is federally designated as critical national infrastructure. In 2014, the formerly aseismic city of Cushing experienced a Mw 4.0 and 4.3 induced earthquake sequence due to wastewater injection. Since then, an M4+ earthquake sequence has occurred annually (October 2014, September 2015, November 2016). Thus far, damage to critical infrastructure has been minimal; however, a larger earthquake could pose significant risk to the Cushing Hub. In addition to inducing earthquakes, wastewater injection also threatens the Cushing Hub through gradual surface uplift. To characterize the impact of wastewater injection on critical infrastructure, we use Differential Interferometric Synthetic Aperture Radar (DInSAR), a satellite radar technique, to observe ground surface displacement in Cushing before and during the induced Mw 5.0 event. Here, we process interferograms of Single Look Complex (SLC) radar data from the European Space Agency (ESA) Sentinel-1A satellite. The preearthquake interferograms are used to create a time series of cumulative surface displacement, while the coseismic interferograms are used to invert for earthquake source characteristics. The time series of surface displacement reveals 4–5.5 cm of uplift across Cushing over 17 months. The coseismic interferogram inversion suggests that the 2016 Mw 5.0 earthquake is shallower than estimated from seismic inversions alone. This shallower source depth should be taken into account in future hazard assessments for regional infrastructure. In addition, monitoring of surface deformation near wastewater injection wells can be used to characterize the subsurface dynamics and implement measures to mitigate damage to critical installations. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Open AccessArticle
Geocoding Error Correction for InSAR Point Clouds
Remote Sens. 2018, 10(10), 1523; https://doi.org/10.3390/rs10101523
Received: 14 August 2018 / Revised: 13 September 2018 / Accepted: 20 September 2018 / Published: 22 September 2018
Cited by 1 | PDF Full-text (11883 KB) | HTML Full-text | XML Full-text
Abstract
Persistent Scatterer Interferometry (PSI) is an advanced multitemporal InSAR technique that is capable of retrieving the 3D coordinates and the underlying deformation of time-coherent scatterers. Various factors degrade the localization accuracy of PSI point clouds in the geocoding process, which causes problems for [...] Read more.
Persistent Scatterer Interferometry (PSI) is an advanced multitemporal InSAR technique that is capable of retrieving the 3D coordinates and the underlying deformation of time-coherent scatterers. Various factors degrade the localization accuracy of PSI point clouds in the geocoding process, which causes problems for interpretation of deformation results and also making it difficult for the point clouds to be compared with or integrated into data from other sensors. In this study, we employ the SAR imaging geodesy method to perform geodetic corrections on SAR timing observations and thus improve the positioning accuracy in the horizontal components. We further utilize geodetic stereo SAR to extract large number of highly precise ground control points (GCP) from SAR images, in order to compensate for the unknown height offset of the PSI point cloud. We demonstrate the applicability of the approach using TerraSAR-X high resolution spotlight images over the city of Berlin, Germany. The corrected results are compared with a reference LiDAR point cloud of Berlin, which confirms the improvement in the geocoding accuracy. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Open AccessArticle
Investigation of Ground Deformation in Taiyuan Basin, China from 2003 to 2010, with Atmosphere-Corrected Time Series InSAR
Remote Sens. 2018, 10(9), 1499; https://doi.org/10.3390/rs10091499
Received: 1 August 2018 / Revised: 3 September 2018 / Accepted: 7 September 2018 / Published: 19 September 2018
Cited by 1 | PDF Full-text (46504 KB) | HTML Full-text | XML Full-text
Abstract
Excessive groundwater exploitation is common through the Taiyuan basin, China, and is well known to result in ground subsidence. However, most ground subsidence studies in this region focus on a single place (Taiyuan city), and thus fail to demonstrate the regional extent of [...] Read more.
Excessive groundwater exploitation is common through the Taiyuan basin, China, and is well known to result in ground subsidence. However, most ground subsidence studies in this region focus on a single place (Taiyuan city), and thus fail to demonstrate the regional extent of the deformation phenomena in the whole basin. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) time series analysis to investigate land subsidence across the entire Taiyuan basin region. Our data set includes a total of 75 ENVISAT ASAR images from two different frames acquired from August 2003 to September 2010 and 33 TerraSAR-X scenes spanning between March 2009 and March 2010. ERA-Interim reanalysis was used to correct the stratified delay to reduce the bias expected from the systematic components of tropospheric delay. The residual delay after correction of stratified delay can be considered as a stochastic component and be mitigated through spatial-temporal filtering. A comparison with MERIS (Medium-Resolution Imaging Spectrometer) measurements indicates that our atmospheric corrections improved agreement over the conventional spatial-temporal filtering by about 20%. The displacement results from our atmosphere-corrected time series InSAR were further validated with continuous GPS data. We found eight subsiding centers in the basin and a surface uplift to the north of Taiyuan city. The causes of ground deformation are analyzed and discussed in relation to gravity data, pre-existing faults, and types of land use. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Open AccessArticle
Evaluation of the Stability of the Darbandikhan Dam after the 12 November 2017 Mw 7.3 Sarpol-e Zahab (Iran–Iraq Border) Earthquake
Remote Sens. 2018, 10(9), 1426; https://doi.org/10.3390/rs10091426
Received: 19 July 2018 / Revised: 2 September 2018 / Accepted: 5 September 2018 / Published: 7 September 2018
Cited by 2 | PDF Full-text (25114 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We used a global positioning system (GPS), levelling, and Sentinel-1 data to evaluate the stability of the Darbandikhan dam in northeast Iraq after the 2017 Mw 7.3 Sarpol-e Zahab earthquake. GPS and levelling datasets collected in March and November 2017 were used to [...] Read more.
We used a global positioning system (GPS), levelling, and Sentinel-1 data to evaluate the stability of the Darbandikhan dam in northeast Iraq after the 2017 Mw 7.3 Sarpol-e Zahab earthquake. GPS and levelling datasets collected in March and November 2017 were used to compute the co-seismic surface displacements of the dam. Sentinel-1 synthetic aperture radar (SAR) images collected between October 2014 and March 2018 were employed to recover the displacement time series of the dam. The large-magnitude displacement gradient on the dam crest hindered the estimation of the co-seismic displacement using this medium-resolution SAR data. However, Sentinel-1 images are sufficient to examine the stability of the dam displacement before and after the earthquake. The results show that the dam was stable between October 2014 and November 2017, but after the earthquake, Sentinel-1 data shows a continuous subsidence of the dam crest between November 2017 and March 2018. To the best knowledge of the authors, this study is the first that utilises InSAR to investigate the behaviour of a dam after a large earthquake. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Open AccessArticle
Imaging Multi-Age Construction Settlement Behaviour by Advanced SAR Interferometry
Remote Sens. 2018, 10(7), 1137; https://doi.org/10.3390/rs10071137
Received: 8 June 2018 / Revised: 11 July 2018 / Accepted: 16 July 2018 / Published: 18 July 2018
Cited by 3 | PDF Full-text (9457 KB) | HTML Full-text | XML Full-text
Abstract
This paper focuses on the application of Advanced Satellite Synthetic Aperture Radar Interferometry (A-DInSAR) to subsidence-related issues, with particular reference to ground settlements due to external loads. Beyond the stratigraphic setting and the geotechnical properties of the subsoil, other relevant boundary conditions strongly [...] Read more.
This paper focuses on the application of Advanced Satellite Synthetic Aperture Radar Interferometry (A-DInSAR) to subsidence-related issues, with particular reference to ground settlements due to external loads. Beyond the stratigraphic setting and the geotechnical properties of the subsoil, other relevant boundary conditions strongly influence the reliability of remotely sensed data for quantitative analyses and risk mitigation purposes. Because most of the Persistent Scatterer Interferometry (PSI) measurement points (Persistent Scatterers, PSs) lie on structures and infrastructures, the foundation type and the age of a construction are key factors for a proper interpretation of the time series of ground displacements. To exemplify a methodological approach to evaluate these issues, this paper refers to an analysis carried out in the coastal/deltaic plain west of Rome (Rome and Fiumicino municipalities) affected by subsidence and related damages to structures. This region is characterized by a complex geological setting (alternation of recent deposits with low and high compressibilities) and has been subjected to different urbanisation phases starting in the late 1800s, with a strong acceleration in the last few decades. The results of A-DInSAR analyses conducted from 1992 to 2015 have been interpreted in light of high-resolution geological/geotechnical models, the age of the construction, and the types of foundations of the buildings on which the PSs are located. Collection, interpretation, and processing of geo-thematic data were fundamental to obtain high-resolution models; change detection analyses of the land cover allowed us to classify structures/infrastructures in terms of the construction period. Additional information was collected to define the types of foundations, i.e., shallow versus deep foundations. As a result, we found that only by filtering and partitioning the A-DInSAR datasets on the basis of the above-mentioned boundary conditions can the related time series be considered a proxy of the consolidation process governing the subsidence related to external loads as confirmed by a comparison with results from a physically based back analysis based on Terzaghi’s theory. Therefore, if properly managed, the A-DInSAR data represents a powerful tool for capturing the evolutionary stage of the process for a single building and has potential for forecasting the behaviour of the terrain–foundation–structure combination. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Open AccessArticle
Estimation of Water Level Changes of Large-Scale Amazon Wetlands Using ALOS2 ScanSAR Differential Interferometry
Remote Sens. 2018, 10(6), 966; https://doi.org/10.3390/rs10060966
Received: 23 May 2018 / Revised: 13 June 2018 / Accepted: 15 June 2018 / Published: 17 June 2018
Cited by 3 | PDF Full-text (7050 KB) | HTML Full-text | XML Full-text
Abstract
Differential synthetic aperture radar (SAR) interferometry (DInSAR) has been successfully used to estimate water level changes (∂h/∂t) over wetlands and floodplains. Specifically, amongst ALOS PALSAR datasets, the fine-beam stripmap mode has been mostly implemented to estimate ∂h/∂t due to its availability of multitemporal [...] Read more.
Differential synthetic aperture radar (SAR) interferometry (DInSAR) has been successfully used to estimate water level changes (∂h/∂t) over wetlands and floodplains. Specifically, amongst ALOS PALSAR datasets, the fine-beam stripmap mode has been mostly implemented to estimate ∂h/∂t due to its availability of multitemporal images. However, the fine-beam observation mode provides limited swath coverage to study large floodplains and wetlands, such as the Amazon floodplains. Therefore, for the first time, this paper demonstrates that ALOS2 ScanSAR data can be used to estimate the large-scale ∂h/∂t in Amazon floodplains. The basic procedures and challenges of DInSAR processing with ALOS2 ScanSAR data are addressed and final ∂h/∂t maps are generated based on the Satellite with ARgos and ALtiKa (SARAL) altimetry’s reference data. This study reveals that the local ∂h/∂t patterns of Amazon floodplains are spatially complex with highly interconnected floodplain channels, but the large-scale (with 350 km swath) ∂h/∂t patterns are simply characterized by river water flow directions. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Open AccessArticle
Mapping and Characterizing Thermal Dilation of Civil Infrastructures with Multi-Temporal X-Band Synthetic Aperture Radar Interferometry
Remote Sens. 2018, 10(6), 941; https://doi.org/10.3390/rs10060941
Received: 24 April 2018 / Revised: 31 May 2018 / Accepted: 4 June 2018 / Published: 14 June 2018
Cited by 1 | PDF Full-text (8048 KB) | HTML Full-text | XML Full-text
Abstract
Temperature variation plays a significant role in the long-term structural behaviour of civil infrastructures, but very few quantitative studies have measured and analysed the infrastructures’ global thermal dilation because of their large sizes and geometric complexities. The modern Differential Synthetic Aperture Radar Interferometry [...] Read more.
Temperature variation plays a significant role in the long-term structural behaviour of civil infrastructures, but very few quantitative studies have measured and analysed the infrastructures’ global thermal dilation because of their large sizes and geometric complexities. The modern Differential Synthetic Aperture Radar Interferometry (DInSAR) technique has great potential in applications of their thermal dilation mapping and characterization due to the techniques’ unique capabilities for use in large areas, with high-resolution, and at low-costs for deformation measurements. However, the practical application of DInSAR in thermal dilation estimation is limited by difficulty in the precise separation from the residual topographic phase and the trend deformation phase. Moreover, due to a lack of thermal dilation characteristics analyses in previous studies, the thermal dilation mechanisms are still unclear to users, which restricts the accurate understanding of the thermal dilation evolution process. Given the above challenges, an advanced multi-temporal DInSAR approach is proposed in this study, and the effectiveness of this approach was presented using three cases studies concerning different infrastructure types. In this method, the coherent, incoherent, and semantic information of structures were combined in order to refine the detection of point-like targets (PTs). Interferometric subsets with small temporal baselines and temperature differences were used for high-resolution topography estimation. A pre-analysis was adopted to determine the transmission direction, spatial pattern, and temporal variation of the thermal dilation. Then, both the traditional least squares estimation and our robust coherence-weighted least squares regression analysis were performed between the time series displacements and the corresponding temperatures to quantitatively estimate the thermal dilation model. The results were verified in terms of the estimated linear thermal dilation coefficient, which indicates the greater reliability of our method. Furthermore, the thermal dilation characteristics of different civil infrastructure types were analysed, which facilitates a greater understanding of the thermal dilation evolution process of civil infrastructures. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Open AccessArticle
Efficient Ground Surface Displacement Monitoring Using Sentinel-1 Data: Integrating Distributed Scatterers (DS) Identified Using Two-Sample t-Test with Persistent Scatterers (PS)
Remote Sens. 2018, 10(5), 794; https://doi.org/10.3390/rs10050794
Received: 8 April 2018 / Revised: 15 May 2018 / Accepted: 17 May 2018 / Published: 19 May 2018
Cited by 3 | PDF Full-text (20077 KB) | HTML Full-text | XML Full-text
Abstract
Combining persistent scatterers (PS) and distributed scatterers (DS) is important for effective displacement monitoring using time-series of SAR data. However, for large stacks of synthetic aperture radar (SAR) data, the DS analysis using existing algorithms becomes a time-consuming process. Moreover, the whole procedure [...] Read more.
Combining persistent scatterers (PS) and distributed scatterers (DS) is important for effective displacement monitoring using time-series of SAR data. However, for large stacks of synthetic aperture radar (SAR) data, the DS analysis using existing algorithms becomes a time-consuming process. Moreover, the whole procedure of DS selection should be repeated as soon as a new SAR acquisition is made, which is challenging considering the short repeat-observation of missions such as Sentinel-1. SqueeSAR is an approach for extracting signals from DS, which first applies a spatiotemporal filter on images and optimizes DS, then incorporates information from both optimized DS and PS points into interferometric SAR (InSAR) time-series analysis. In this study, we followed SqueeSAR and implemented a new approach for DS analysis using two-sample t-test to efficiently identify neighboring pixels with similar behaviour. We evaluated the performance of our approach on 50 Sentinel-1 images acquired over Trondheim in Norway between January 2015 and December 2016. A cross check on the number of the identified neighboring pixels using the Kolmogorov–Smirnov (KS) test, which is employed in the SqueeSAR approach, and the t-test shows that their results are strongly correlated. However, in comparison to KS-test, the t-test is less computationally intensive (98% faster). Moreover, the results obtained by applying the tests under different SAR stack sizes from 40 to 10 show that the t-test is less sensitive to the number of images. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Open AccessFeature PaperArticle
Multi-Temporal InSAR Structural Damage Assessment: The London Crossrail Case Study
Remote Sens. 2018, 10(2), 287; https://doi.org/10.3390/rs10020287
Received: 18 December 2017 / Revised: 5 February 2018 / Accepted: 7 February 2018 / Published: 13 February 2018
Cited by 14 | PDF Full-text (2733 KB) | HTML Full-text | XML Full-text
Abstract
Spaceborne multi-temporal interferometric synthetic aperture radar (MT-InSAR) is a monitoring technique capable of extracting line of sight (LOS) cumulative surface displacement measurements with millimeter accuracy. Several improvements in the techniques and datasets quality led to more effective, near real time assessment and response, [...] Read more.
Spaceborne multi-temporal interferometric synthetic aperture radar (MT-InSAR) is a monitoring technique capable of extracting line of sight (LOS) cumulative surface displacement measurements with millimeter accuracy. Several improvements in the techniques and datasets quality led to more effective, near real time assessment and response, and a greater ability of constraining dynamically changing physical processes. Using examples of the COSMO-SkyMed (CSK) system, we present a methodology that bridges the gaps between MT-InSAR and the relative stiffness method for tunnel-induced subsidence damage assessment. The results allow quantification of the effect of the building on the settlement profile. As expected the greenfield deformation assessment tends to provide a conservative estimate in the majority of cases (~71% of the analyzed buildings), overestimating tensile strains up to 50%. With this work we show how these two techniques in the field of remote sensing and structural engineering can be synergistically used to complement and replace the traditional ground based analysis by providing an extended coverage and a temporally dense set of data. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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