Special Issue "InSAR in Remote Sensing"

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

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editor

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 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

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

Published Papers (2 papers)

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Research

Open AccessArticle
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
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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Open AccessArticle
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
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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