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Remote Sensing
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Advances in InSAR Processing: Algorithmic Developments and Diverse Applications
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Advances in InSAR Processing: Algorithmic Developments and Diverse Applications

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

Deadline for manuscript submissions: 29 July 2025 | Viewed by 1055

Special Issue Editors

Dr. Yasser Maghsoudi

E-Mail Website
Guest Editor
Department of Earth and Environmental Sciences, University of Exeter, Penryn Campus, Penryn, Cornwall TR10 9FE, UK
Interests: InSAR algorithm development; geohazard monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Francesca Cigna

E-Mail Website
Guest Editor
Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Via del Fosso del Cavaliere 100, 00133 Rome, Italy
Interests: landscape evolution; geophysical hazards; archaeology; cultural heritage; remote sensing; earth observation; InSAR; landslides; land subsidence; ground instability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of interferometric synthetic aperture radar (InSAR) continues to evolve, driven by significant advances in processing algorithms that are enhancing accuracy and broadening the range of applications. This Special Issue will focus on the latest algorithmic advancements in InSAR that are crucial for achieving millimetric accuracy in ground deformation measurements. Key developments include phase unwrapping techniques, persistent scatterer InSAR (PS-InSAR), small baseline subset (SBAS), advanced atmospheric correction techniques, including tropospheric and ionospheric delay correction, and non-closure phase bias correction.

Additionally, the recent application of deep learning techniques in InSAR is revolutionizing how we approach error detection, correction, and data processing. Notably, deep learning has been applied to improve phase unwrapping algorithms, significantly enhancing accuracy in complex areas with high noise or decorrelation. These techniques are also utilized in multi-temporal InSAR processing, automatic displacement detection, and hazard classification, enabling more efficient and precise monitoring of slow-moving hazards, such as land subsidence, volcanic deformation, and landslides.

This Special Issue will highlight how these algorithmic innovations are broadening the scope of InSAR applications. These include climate change studies, soil moisture estimation, coastal monitoring, structural health monitoring, and multi-hazard risk assessment, further enhancing the ability to monitor and assess complex geophysical processes.

Dr. Yasser Maghsoudi
Dr. Francesca Cigna
Guest Editors

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 submissions that pass pre-check are 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 2700 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

  • InSAR algorithm development
  • phase unwrapping
  • deep learning
  • ground deformation
  • persistent scatterer InSAR (PS-InSAR)
  • small baseline subset (SBAS)
  • atmospheric correction

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Published Papers (2 papers)

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Research

30 pages, 23425 KiB  
Article
Monitoring Vertical Urban Growth in Rapidly Developing Cities with Persistent Scatterer Interferometry: A Multi-Temporal Assessment with COSMO-SkyMed Data in Wuhan, China
by Zeeshan Afzal, Timo Balz, Francesca Cigna and Deodato Tapete
Remote Sens. 2025, 17(11), 1915; https://doi.org/10.3390/rs17111915 - 31 May 2025
Viewed by 313
Abstract
Rapid urbanization has transformed cityscapes worldwide, yet vertical urban growth (VUG) receives less attention than horizontal expansion. This study mapped and analyzed VUG patterns in Wuhan, China, from 2012 to 2020 based on a Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) dataset derived [...] Read more.
Rapid urbanization has transformed cityscapes worldwide, yet vertical urban growth (VUG) receives less attention than horizontal expansion. This study mapped and analyzed VUG patterns in Wuhan, China, from 2012 to 2020 based on a Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) dataset derived from a long time series of 375 COSMO-SkyMed SAR images. The methodology involved full-stack processing (analyzing all 375 images for a stable reference), sub-stack processing (independently processing sequential image subsets to track temporal changes), and post-processing to extract persistent scatterer (PS) candidates, estimate building heights, and analyze temporal changes. Validation was conducted through drone surveys and ground measurements in the Hanyang district. Results revealed substantial vertical expansion in central districts, with Hanyang experiencing a 66-fold increase in areas with buildings exceeding 90 m in height, while Hongshan district saw a 34-fold increase. Peripheral districts instead displayed more modest growth. Time series analysis and 3D visualization captured VUG temporal dynamics, identifying specific rapidly transforming urban sectors within Hanyang. Although the study is focused on one city with accuracy assessed on a spatially confined sample of more than 500 buildings, the findings suggest that PSInSAR height estimates from high-resolution SAR imagery can complement global settlement datasets (e.g., Global Human Settlement Layer, GHSL) in order to achieve better accuracy for individual building heights. Validation generally confirmed the accuracy of PSInSAR-derived height estimates, though challenges remain with noise and the distribution of PS. The location of PS along the building instead of the building rooftops can affect height estimation precision. Full article
(This article belongs to the Special Issue Advances in InSAR Processing: Algorithmic Developments and Diverse Applications)
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26 pages, 19937 KiB  
Article
NBDNet: A Self-Supervised CNN-Based Method for InSAR Phase and Coherence Estimation
by Hongxiang Li, Jili Wang, Chenguang Ai, Yulun Wu and Xiaoyuan Ren
Remote Sens. 2025, 17(7), 1181; https://doi.org/10.3390/rs17071181 - 26 Mar 2025
Viewed by 447
Abstract
Phase denoising constitutes a critical component of the synthetic aperture radar interferometry (InSAR) processing chain, where noise suppression and detail preservation are two mutually constraining objectives. Recently, deep learning has attracted considerable interest due to its promising performance in the field of image [...] Read more.
Phase denoising constitutes a critical component of the synthetic aperture radar interferometry (InSAR) processing chain, where noise suppression and detail preservation are two mutually constraining objectives. Recently, deep learning has attracted considerable interest due to its promising performance in the field of image denoising. In this paper, a Neighbor2Neighbor denoising network (NBDNet) is proposed, which is capable of simultaneously estimating phase and coherence in both single-look and multi-look cases. Specifically, repeat-pass PALSAR real interferograms encompassing a diverse range of coherence, fringe density, and terrain features are used as the training dataset, and the novel Neighbor2Neighbor self-supervised training framework is leveraged. The Neighbor2Neighbor framework eliminates the necessity of noise-free labels, simplifying the training process. Furthermore, rich features can be learned directly from real interferograms. In order to validate the denoising capability and generalization ability of the proposed NBDNet, simulated data, repeat-pass data from Sentinel-1 Interferometric Wide (IW) swath mode, and single-pass data from Hongtu-1 stripmap mode are used for phase denoising experiments. The results demonstrate that NBDNet performs well in terms of noise suppression, detail preservation and computation efficiency, validating its potential for high-precision and high-resolution topography reconstruction. Full article
(This article belongs to the Special Issue Advances in InSAR Processing: Algorithmic Developments and Diverse Applications)
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