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Advances in InSAR Processing: Algorithmic Developments and Diverse Applications

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 12335

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Editors

Dr. Yasser Maghsoudi

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Guest Editor

Centre for Environmental Mathematics, Department of Earth and Environmental Sciences, University of Exeter, Penryn Campus, Cornwall TR10 9FE, UK
Interests: time-series; earth observation; InSAR technology
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Dr. Francesca Cigna

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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
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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

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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 (7 papers)

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Research

26 pages, 6724 KB

Open AccessArticle

Residual Noise Learning for Atmospheric Correction of InSAR Unwrapped Maps

by Yuchen Li and Takeshi Sagiya

Remote Sens. 2026, 18(14), 2359; https://doi.org/10.3390/rs18142359 - 15 Jul 2026

Viewed by 544

Abstract

Atmospheric artifacts can obscure tectonic deformation in interferometric synthetic aperture radar (InSAR) observations, while conventional correction methods often depend on external atmospheric data. This study proposes a supervised residual-learning framework that directly predicts noise components in unwrapped InSAR maps rather than reconstructing deformation signals. Physically informed synthetic datasets were generated by combining Okada and Mogi deformation models with topography-correlated tropospheric delays, spatially correlated turbulent noise, and long-wavelength ramps. A network-depth sensitivity analysis identified a 20-layer denoising convolutional neural network as the optimal balance between accuracy and model complexity. Tests on independent synthetic datasets showed that the model reliably distinguished deformation from noise when the signal-to-noise ratio exceeded approximately 10⁻¹, whereas performance degraded under extremely noise-dominated conditions. The framework was further evaluated using ALOS/PALSAR and ALOS-2/PALSAR-2 observations of post-eruptive deformation at Mt. Ontake, Japan, and coseismic deformation associated with the 2009 L’Aquila earthquake, Italy. Compared with uncorrected, GACOS-corrected, and linear-corrected results, the CNN correction reduced topography-correlated and long-wavelength artifacts, improved temporal consistency, and generally achieved closer agreement with GNSS observations. These results suggest that residual noise learning provides an efficient approach for automatic atmospheric correction of unwrapped InSAR observations, with the potential for transferability across different deformation-source settings. Full article

(This article belongs to the Special Issue Advances in InSAR Processing: Algorithmic Developments and Diverse Applications)

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21 pages, 26356 KB

Open AccessArticle

Fringe-Enhanced Phase Unwrapping Method Based on an Iterative Bayes–Sard Quadrature Kalman Filter

by Mingsi Lin, Xiangzhen Zeng and Xiaomao Chen

Remote Sens. 2026, 18(10), 1661; https://doi.org/10.3390/rs18101661 - 21 May 2026

Viewed by 493

Abstract

Phase unwrapping plays a vital role in interferometric synthetic aperture radar (InSAR) processing. However, the presence of noise can introduce inconsistencies in phase discontinuities, giving rise to residue points that may cause unwrapping errors. To address this challenge, this paper for the first time applies the Bayes–Sard quadrature transform to the phase unwrapping problem and proposes an iterative Bayes–Sard quadrature Kalman filter phase unwrapping method (IBSQKF). In contrast to the conventional unscented Kalman filter algorithm, the Bayes–Sard moment transform can quantify the additional uncertainty introduced by quadrature errors. Through integration with the proposed iterative strategy, it enables more accurate calibration of state estimation and effectively reduces the root mean square error. To further enhance unwrapping accuracy, a multi-level and multi-scale feature fusion neural network (PFTNet) is developed as a pre-filtering module to independently process the real and imaginary components of the complex interferometric phase representation, which can effectively enhance the clarity of the interferometric fringes. By integrating PFTNet with IBSQKF, a complete phase unwrapping framework (PFT-IBSQKF) is constructed to further improve unwrapping accuracy. Experiments on both simulated and real data demonstrate that IBSQKF can reliably restore phase continuity, while PFT-IBSQKF can further reduce unwrapping errors, especially in low signal-to-noise-ratio or fringe-blurred scenarios. Despite the introduction of the iterative strategy, the proposed framework still maintains an acceptable computational cost while achieving high unwrapping accuracy. Full article

(This article belongs to the Special Issue Advances in InSAR Processing: Algorithmic Developments and Diverse Applications)

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19 pages, 5637 KB

Open AccessArticle

Can the Subsidence of High-Fill Airports Be Avoided Using Engineering Approaches? A National-Scale SBAS-InSAR-Based Examination in China

by Meixuan Lan, Qiong Wu, Jun Wang, Liwei Gong, Na Ta and Kuiwen Wang

Remote Sens. 2026, 18(4), 661; https://doi.org/10.3390/rs18040661 - 21 Feb 2026

Viewed by 682

Abstract

With the rapid expansion of airport construction projects in China, high-fill airports are frequently built under complex geological conditions, where the high risk of surface stability may significantly affect flight safety and operational costs. In this study, 17 high-fill airports and 11 non-high-fill airports across China, all characterized by high subsidence risks, were selected to investigate vertical ground deformation. Utilizing multi-temporal Sentinel-1A radar imagery spanning from 2017 to 2024, Small Baseline Subset InSAR (SBAS-InSAR) was employed to retrieve the annual average deformation velocities and time-series cumulative displacements. The results revealed that among the selected sites, only 25% were relatively stable, while the others exhibited significant deformation characteristics. Notably, high-fill airports demonstrated greater deformation magnitudes compared to those in plain areas, especially in the area of prevalent slope subsidence. In addition, significantly positive correlation was found between fill height and deformation magnitude, while differential settlement was widespread in runway zones. Furthermore, foundations involving special ground conditions manifested continuedly and distinct deformation patterns despite ground treatments. This study demonstrates the limitations of current engineering approaches in completely eliminating airport deformation, and offers valuable insights for the site selection, engineering design, and maintenance of high-fill airports. Full article

(This article belongs to the Special Issue Advances in InSAR Processing: Algorithmic Developments and Diverse Applications)

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19 pages, 15163 KB

Open AccessArticle

Enhanced Co-Registration Method for Long-Baseline SAR Images

by Dong Zeng, Haiqiang Fu, Jianjun Zhu, Qijin Han, Aichun Wang, Mingxia Zhang, Kefu Wu, Zhiwei Liu and Zhiwei Li

Remote Sens. 2025, 17(24), 4034; https://doi.org/10.3390/rs17244034 - 15 Dec 2025

Viewed by 1220

Abstract

Accurate synthetic aperture radar (SAR) image co-registration is a crucial procedure for high-quality interferometry and its associated applications. Neglecting the effect of terrain elevation, conventional techniques employ simple polynomial models to achieve accurate co-registration between SAR image pairs during fine co-registration processing. However, these methods become inapplicable for tugged terrain, especially under longer spatial baseline conditions. On the basis of this, we introduced an elevation-dependent term into the conventional fine co-registration model to compensate for local offsets caused by variable topography. As a result, a new SAR image fine co-registration method was proposed. To validate the proposed method, experiments were conducted using data from China’s LuTan-1 satellite in two typical study areas (Madrid, Spain, and Shannan, China), across diverse land-cover types and terrain conditions. At the Madrid test site, the proposed co-registration algorithm can effectively improve the phase quality (average coherence improves from 0.57 to 0.77), and topography accuracy (quantified by root-mean-square-error, RMSE) improved from 3.67 m to 3.59 m in mountainous regions, and it shows similar performance in relatively flat areas to that of the conventional methods. At the Shannan test site, characterized by rugged terrain, the average coherence of the interferogram obtained by our method increased from 0.32 to 0.48 compared to the conventional co-registration approach. Against the reference topographic data, the InSAR DEM retrieved by our proposed method achieved an RMSE of 6.31 m, indicating an improvement of 23%. This study provides an effective method to enhance the quality of co-registration and interferometry in areas with complex terrain. Full article

(This article belongs to the Special Issue Advances in InSAR Processing: Algorithmic Developments and Diverse Applications)

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21 pages, 15482 KB

Open AccessArticle

InSAR Detection of Slow Ground Deformation: Taking Advantage of Sentinel-1 Time Series Length in Reducing Error Sources

by Machel Higgins and Shimon Wdowinski

Remote Sens. 2025, 17(14), 2420; https://doi.org/10.3390/rs17142420 - 12 Jul 2025

Cited by 4 | Viewed by 2925

Abstract

Using interferometric synthetic aperture radar (InSAR) to observe slow ground deformation can be challenging due to many sources of error, with tropospheric phase delay and unwrapping errors being the most significant. While analytical methods, weather models, and data exist to mitigate tropospheric error, most of these techniques are unsuitable for all InSAR applications (e.g., complex tropospheric mixing in the tropics) or are deficient in spatial or temporal resolution. Likewise, there are methods for removing the unwrapping error, but they cannot resolve the true phase when there is a high prevalence (>40%) of unwrapping error in a set of interferograms. Applying tropospheric delay removal techniques is unnecessary for C-band Sentinel-1 InSAR time series studies, and the effect of unwrapping error can be minimized if the full dataset is utilized. We demonstrate that using interferograms with long temporal baselines (800 days to 1600 days) but very short perpendicular baselines (<5 m) (LTSPB) can lower the velocity detection threshold to 2 mm y⁻¹ to 3 mm y⁻¹ for long-term coherent permanent scatterers. The LTSPB interferograms can measure slow deformation rates because the expected differential phases are larger than those of small baselines and potentially exceed the typical noise amplitude while also reducing the sensitivity of the time series estimation to the noise sources. The method takes advantage of the Sentinel-1 mission length (2016 to present), which, for most regions, can yield up to 300 interferograms that meet the LTSPB baseline criteria. We demonstrate that low velocity detection can be achieved by comparing the expected LTSPB differential phase measurements to synthetic tests and tropospheric delay from the Global Navigation Satellite System. We then characterize the slow (~3 mm/y) ground deformation of the Socorro Magma Body, New Mexico, and the Tampa Bay Area using LTSPB InSAR analysis. The method we describe has implications for simplifying the InSAR time series processing chain and enhancing the velocity detection threshold. Full article

(This article belongs to the Special Issue Advances in InSAR Processing: Algorithmic Developments and Diverse Applications)

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30 pages, 23425 KB

Open AccessArticle

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

Cited by 2 | Viewed by 2502

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 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 KB

Open AccessArticle

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

Cited by 9 | Viewed by 2271

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 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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