Special Issue "InSAR in Remote Sensing"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2021).

Special Issue Editor

Prof. Dr. Duk-jin Kim
E-Mail Website
Guest Editor
School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea
Interests: disaster monitoring; deep learning; radar image processing; environmental changes
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the past two decades, the interferometric SAR (InSAR) technique was widely applied in the field of remote sensing to measure Earth’s surface deformation due to various natural and man-made disasters such as earthquakes, landslides, groundwater depletion, underground mining. In recent times, the availability of high spatial and temporal coverage of SAR data has provided the additional advantage of time-series analysis. Currently, many satellites deliver SAR data with high spatial resolution (less than 5 m) with dual and full polarizations, therefore offering a unique opportunity to design precise displacement maps and evaluate damages on artificial structures such as bridges, dams, buildings, etc..

Therefore, high-resolution displacement products are the main current topic of interest in the development of InSAR techniques and will allow to better understand the processes of man-made hazards in urban areas and infrastructures. This Special Issue “InSAR in Remote Sensing” will focus on: (1) Innovative applications using time-series algorithms such as Persistent scatterers InSAR (PSI) and Small baseline subset (SBAS) that emphasize the importance of high-spatial-resolution SAR data for high-resolution InSAR products in urban areas; (2) The development on new time-series data processing algorithms by utilizing the capacity of dual- and full-polarization SAR data.

For this Special Issue, we are inviting the submission of original articles focused on, but not exclusively, the following topics:

  • Monitoring Infrastructure deformations
  • Advances in PSI and SBAS algorithms for urban deformation monitoring
  • PSI results using polarimetric data
  • Urban deformation monitoring using SAR tomography
  • Comparative assessments of Conventional InSAR (PSI, SBAS) and SAR tomography
  • Groundwater depletion
  • Sinkholes

Prof. Duk-jin Kim
Guest Editor

Manuscript Submission Information

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Keywords

  • Interferometric SAR
  • High-resolution SAR
  • Time-series analysis
  • Infrastructures
  • Polarimetric SAR Interferometry
  • Deformation

Published Papers (6 papers)

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Research

Communication
Position Inversion of Goafs in Deep Coal Seams Based on DS-InSAR Data and the Probability Integral Methods
Remote Sens. 2021, 13(15), 2898; https://doi.org/10.3390/rs13152898 - 23 Jul 2021
Viewed by 391
Abstract
The goafs caused by coal mining cause great harm to the surface farmland, buildings, and personal safety. The existing monitoring methods cost a lot of workforce and material resources. Therefore, this paper proposes an inversion approach for establishing the locations of underground goafs [...] Read more.
The goafs caused by coal mining cause great harm to the surface farmland, buildings, and personal safety. The existing monitoring methods cost a lot of workforce and material resources. Therefore, this paper proposes an inversion approach for establishing the locations of underground goafs and the parameters of the probability integral method (PIM), thus integrating distributed scatter interferometric synthetic aperture radar (DS-InSAR) data and the PIM. Firstly, a large amount of surface deformation observation data above the goaf are obtained by DS-InSAR, and the line-of-sight deformation is regarded as the true value. Secondly, according to the obtained surface deformations, the ranges of eight goaf location parameters and three PIM parameters are set. Thirdly, a correlation function between the surface deformation and the underground goaf location is constructed. Finally, a particle swarm optimization algorithm is used to search for the optimal parameters in the range of the set parameters to meet the requirement for minimum error between the surface deformation calculated by PIM and the line-of-sight deformation obtained by DS-InSAR. These optimal parameters are thus regarded as the real values of the position of the underground goaf and the PIM parameters. The simulation results show that the maximum relative error between the position of the goaf and the PIM parameters is 2.11%. Taking the 93,604 working face of the Zhangshuanglou coal mine in the Peibei mining area as the research object and 12 Sentinel-1A images as the data source, the goaf location and PIM parameters of the working face were successfully inverted. The inversion results show that the maximum relative error in the goaf location parameters was 16.61%, and the maximum relative error in the PIM parameters was 26.67%. Full article
(This article belongs to the Special Issue InSAR in Remote Sensing)
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Article
Monitoring the Vertical Land Motion of Tide Gauges and Its Impact on Relative Sea Level Changes in Korean Peninsula Using Sequential SBAS-InSAR Time-Series Analysis
Remote Sens. 2021, 13(1), 18; https://doi.org/10.3390/rs13010018 - 22 Dec 2020
Cited by 1 | Viewed by 1206
Abstract
The relative sea-level changes from tide gauges in the Korean peninsula provide essential information to understand the regional and global mean sea-level changes. Several corrections to raw tide gauge records are required to account for coastal vertical land motion (VLM), regional and local [...] Read more.
The relative sea-level changes from tide gauges in the Korean peninsula provide essential information to understand the regional and global mean sea-level changes. Several corrections to raw tide gauge records are required to account for coastal vertical land motion (VLM), regional and local coastal variability. However, due to the lack of in-situ measurements such as leveling data and the Global Navigation Satellite System (GNSS), making precise assessments of VLM at the tide gauges is still challenging. This study aims to address the above limitation to assess the VLM in the Korean tide gauges using the time-series Interferometric Synthetic Aperture Radar (InSAR) technique. For 10 tide gauges selected in the Korean peninsula, we applied the Stanford Method for Persistent Scatterers (StaMPS)—Small Baseline Subset (SBAS) method to C-band Sentinel-1 A/B Synthetic Aperture Radar (SAR) data acquired during 2014/10–2020/05, with the novel sequential interferograms pair selection approach to increase the slowly decorrelating filtered phase (SDFP) pixels density near the tide gauges. Our findings show that overall the tide gauges in the Korean peninsula are stable, besides the largest VLM observed at Pohang tide gauge station (East Sea) of about −26.02 mm/year; also, higher rates of uplift (>1 mm/year) were observed along the coast of Yellow Sea (Incheon TG and Boryeong TG) and higher rates of subsidence (<−2 mm/year) were observed at Jeju TG and Seogwipo TG. Our approach estimates the rate of VLM at selected tide gauges with an unprecedented spatial and temporal resolution and is applicable when the in-situ and GNSS observations are not available. Full article
(This article belongs to the Special Issue InSAR in Remote Sensing)
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Article
A Phase Filtering Method with Scale Recurrent Networks for InSAR
Remote Sens. 2020, 12(20), 3453; https://doi.org/10.3390/rs12203453 - 21 Oct 2020
Cited by 1 | Viewed by 752
Abstract
Phase filtering is a key issue in interferometric synthetic aperture radar (InSAR) applications, such as deformation monitoring and topographic mapping. The accuracy of the deformation and terrain height is highly dependent on the quality of phase filtering. Researchers are committed to continuously improving [...] Read more.
Phase filtering is a key issue in interferometric synthetic aperture radar (InSAR) applications, such as deformation monitoring and topographic mapping. The accuracy of the deformation and terrain height is highly dependent on the quality of phase filtering. Researchers are committed to continuously improving the accuracy and efficiency of phase filtering. Inspired by the successful application of neural networks in SAR image denoising, in this paper we propose a phase filtering method that is based on deep learning to efficiently filter out the noise in the interferometric phase. In this method, the real and imaginary parts of the interferometric phase are filtered while using a scale recurrent network, which includes three single scale subnetworks based on the encoder-decoder architecture. The network can utilize the global structural phase information contained in the different-scaled feature maps, because RNN units are used to connect the three different-scaled subnetworks and transmit current state information among different subnetworks. The encoder part is used for extracting the phase features, and the decoder part restores detailed information from the encoded feature maps and makes the size of the output image the same as that of the input image. Experiments on simulated and real InSAR data prove that the proposed method is superior to three widely-used phase filtering methods by qualitative and quantitative comparisons. In addition, on the same simulated data set, the overall performance of the proposed method is better than another deep learning-based method (DeepInSAR). The runtime of the proposed method is only about 0.043s for an image with a size of 1024×1024 pixels, which has the significant advantage of computational efficiency in practical applications that require real-time processing. Full article
(This article belongs to the Special Issue InSAR in Remote Sensing)
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Article
Monitoring Littoral Platform Downwearing Using Differential SAR Interferometry
Remote Sens. 2020, 12(19), 3243; https://doi.org/10.3390/rs12193243 - 06 Oct 2020
Cited by 1 | Viewed by 1310
Abstract
A methodology for the remotely sensed monitoring, measurement and quantification of littoral zone platform downwearing has been developed and is demonstrated, using Persistent Scatterer Interferometric Synthetic Aperture Radar data and analysis. The research area is a 30 km section of coast in East [...] Read more.
A methodology for the remotely sensed monitoring, measurement and quantification of littoral zone platform downwearing has been developed and is demonstrated, using Persistent Scatterer Interferometric Synthetic Aperture Radar data and analysis. The research area is a 30 km section of coast in East Sussex, UK. This area combines a range of coastal environments and is characterised by the exposure of chalk along the cliffs and coastal platform. Persistent Scatterer Interferometry (PSI) has been employed, using 3.5 years of Sentinel-1 SAR data. The results demonstrate an average ground level change of −0.36 mm a−1 across the research area, caused by platform downwearing. Protected sections of coast are downwearing at an average of −0.33 mm a−1 compared to unprotected sections, which are downwearing more rapidly at an average rate of −1.10 mm a−1. The material properties of the chalk formations in the platform were considered, and in unprotected areas the weakest chalk types eroded at higher rates (−0.66 mm a−1) than the more resistant formations (−0.53 mm a−1). At a local scale, results were achieved in three studies to demonstrate variations between urban and rural environments. Individual persistent scatterer point values provided a near-continuous sequence of measurements, which allowed the effects of processes to be evaluated. The results of this investigation show an effective way of retrospective and ongoing monitoring of platform downwearing, erosion and other littoral zone processes, at regional, local and point-specific scales. Full article
(This article belongs to the Special Issue InSAR in Remote Sensing)
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Article
DeepInSAR—A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation
Remote Sens. 2020, 12(14), 2340; https://doi.org/10.3390/rs12142340 - 21 Jul 2020
Cited by 6 | Viewed by 1553
Abstract
Over the past decade, using Interferometric Synthetic Aperture Radar (InSAR) remote sensing technology for ground displacement detection has become very successful. However, during the acquisition stage, microwave signals reflected from the ground and received by the satellite are contaminated, for example, due to [...] Read more.
Over the past decade, using Interferometric Synthetic Aperture Radar (InSAR) remote sensing technology for ground displacement detection has become very successful. However, during the acquisition stage, microwave signals reflected from the ground and received by the satellite are contaminated, for example, due to undesirable material reflectance and atmospheric factors, and there is no clean ground truth to discriminate these noises, which adversely affect InSAR phase computation. Accurate InSAR phase filtering and coherence estimation are crucial for subsequent processing steps. Current methods require expert supervision and expensive runtime to evaluate the quality of intermediate outputs, limiting the usability and scalability in practical applications, such as wide area ground displacement monitoring and predication. We propose a deep convolutional neural network based model DeepInSAR to intelligently solve both phase filtering and coherence estimation problems. We demonstrate our model’s performance using simulated and real data. A teacher-student framework is introduced to handle the issue of missing clean InSAR ground truth. Quantitative and qualitative evaluations show that our teacher-student approach requires less input but can achieve better results than its stack-based teacher method even on new unseen data. The proposed DeepInSAR also outperforms three other top non-stack based methods in time efficiency without human supervision. Full article
(This article belongs to the Special Issue InSAR in Remote Sensing)
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Article
Potential of Using Phase Correlation in Distributed Scatterer InSAR Applied to Built Scenarios
Remote Sens. 2020, 12(4), 686; https://doi.org/10.3390/rs12040686 - 19 Feb 2020
Cited by 1 | Viewed by 1134
Abstract
The improved spatial resolution of Synthetic Aperture Radar (SAR) images from newly launched sensors has promoted a more frequent use of distributed scatterer (DS) interferometry (DSI) in urban monitoring, pursuing sufficient and detailed measurements. However, the commonly used statistical methods for homogeneous pixel [...] Read more.
The improved spatial resolution of Synthetic Aperture Radar (SAR) images from newly launched sensors has promoted a more frequent use of distributed scatterer (DS) interferometry (DSI) in urban monitoring, pursuing sufficient and detailed measurements. However, the commonly used statistical methods for homogeneous pixel clustering by exploring amplitude information are firstly, computationally intensive; furthermore, their necessity when applied to high-coherent built scenarios is little discussed in the literature. This paper explores the potential of using phase information for the detection of homogeneous pixels on built surfaces. We propose a simple phase-correlated pixel (PCP) clustering and introduce a coherence-weighted phase link (WPL), i.e., PCPWPL, to pursue a faster processing of interferogram phase denoising. Rather than relying on the statistical tests of amplitude characteristics, we exploit vector correlation in the complex domain to identify PCPs with similar phase observations, thus, avoiding the intensive hypothesis test. A coherence-weighted phase linking is applied for DS phase reconstruction. The estimation of geophysical parameters, e.g., deformation, is completed using an integrated network of persistent scatterers (PS) and DS. Efficiency of the proposed method is fairly illustrated by both synthetic and real data experiments. Pros and cons of the proposed PCPWPL were analyzed with the comparison to a conventional amplitude-based strategy using an X-band CosmoSkyMed dataset. It is demonstrated that the use of phase correlation is sufficient for DS monitoring in built scenarios, with equivalent measurement quantity and cheaper computational cost. Full article
(This article belongs to the Special Issue InSAR in Remote Sensing)
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