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Remote Sensing
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Imaging Geodesy and Infrastructure Monitoring II
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Imaging Geodesy and Infrastructure Monitoring II

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 (31 March 2024) | Viewed by 4098

Special Issue Editors

Dr. Xiaowen Wang

E-Mail Website
Guest Editor
Department of Surveying and Geo-Informatics, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
Interests: InSAR; imaging geodesy; ground deformation; geohazards; earthquakes and volcanoes; mountain permafrost
Prof. Dr. Keren Dai

E-Mail Website
Guest Editor
1. College of Earth Science, Chengdu University of Technology, Chengdu, China
2. State Key Laboratory of Geological Disaster Prevention and Geological Environmental Protection, Chengdu University of Technology, Chengdu, China
Interests: SAR remote sensing; time series InSAR; landslides; land subsidence; geological disaster
Dr. Jie Dong

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Interests: InSAR; geological disaster; landslides; land subsidence; GBSAR
Special Issues, Collections and Topics in MDPI journals
Dr. Rui Zhang

E-Mail Website
Guest Editor
Department of Surveying and Geo-Informatics, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
Interests: radar remote sensing; GB-InSAR; mechine learning; image interpreation; hazard assesment
Prof. Dr. Roberto Tomás

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Alicante, Campus de San Vicente del Raspeig s/n, 03080 Alicante, Spain
Interests: rock mechanics; landslides; infrastructures; remote sensing; geotechnical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The significance of detailed information about the Earth's shape and deformation, especially at the surface, is widely recognized for understanding underlying processes and aiding further geohazard mitigation. In recent years, geodetic imaging techniques, such as LiDAR scanning, structure from motion (SfM) with UAV imagery, satellite/ground-based Interferometric Synthetic Aperture Radar (InSAR), sub-pixel offset tracking with optical/SAR images, and the difference of digital elevation models (DEM) acquired from remote and in-situ instruments, have achieved remarkable advancements. Notably, given that different types of geodetic measurements operate on diverse temporal and spatial scales, the integration of multiple observations has demonstrated its effectiveness in characterizing, monitoring, and assessing the state or changes of the Earth's surface. However, the application of geodetic imaging techniques within the civil engineering community, especially for hazard assessment and mitigation, has yet to be fully explored and utilized.

The primary objective of this special issue is to showcase the progress of geodetic imaging techniques and their scientific applications in monitoring infrastructures, with a particular focus on hazard assessment and mitigation related to geohazards (e.g., landslides, earthquakes, volcanoes) or environmental changes (e.g., permafrost degradation, floods). We encourage submissions on the theory and method advancements for geodetic imaging techniques, as well as numerical modeling and laboratory experiments. Topics of interest include but are not limited to:

  • SAR/InSAR data processing methods and applications in urban regions
  • Theory and methods on in-situ geodetic imaging data acquisition, processing, and analysis
  • Theory and methods on different geodetic imaging data assimilation
  • Stability analysis of infrastructure assisted by geodetic imaging observations
  • Geohazard monitoring and resilience assessment of infrastructures

Dr. Xiaowen Wang
Prof. Dr. Keren Dai
Dr. Jie Dong
Dr. Rui Zhang
Prof. Dr. Roberto Tomás
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

  • imaging geodesy
  • InSAR
  • GB-InSAR
  • pixel-offset tracking
  • DEM differencing
  • SfM
  • infrastructures
  • geohazards
  • data assimilation
  • hazard assessment and mitigation
  • stability analysis

Related Special Issue

Published Papers (6 papers)

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18 pages, 41235 KiB  
Article
Exploring the InSAR Deformation Series Using Unsupervised Learning in a Built Environment
by Mengshi Yang, Menghua Li, Cheng Huang, Ruisi Zhang and Rui Liu
Remote Sens. 2024, 16(8), 1375; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16081375 - 13 Apr 2024
Viewed by 310
Abstract
As a city undergoes large-scale construction and expansion, there is an urgent need to monitor the stability of the ground and infrastructure. The time-series InSAR technique is an effective tool for measuring surface displacements. However, interpreting these displacements in a built environment, where [...] Read more.
As a city undergoes large-scale construction and expansion, there is an urgent need to monitor the stability of the ground and infrastructure. The time-series InSAR technique is an effective tool for measuring surface displacements. However, interpreting these displacements in a built environment, where observed displacements consist of mixed signals, poses a challenge. This study uses principal component analysis (PCA) and the k-means clustering method for exploring deformation series within an unsupervised learning context. The PCA method extracts the dominant components in deformation series, whereas the clustering method identifies similar deformation series. This method was tested on Kunming City (KMC) using C-band Sentinel-1, X-band TerraSAR-X, and L-band ALOS-2 PALSAR-2 data acquired between 2017 to 2022. The experiment demonstrated that the suggested unsupervised learning approach can group PS points with similar kinematic characteristics. Five types of deformation kinematic characteristics were discovered in the three SAR datasets: upward, slight upward, stability, slight downward, and downward. According to the results, less than 20% of points exhibit significant motion trends, whereas 50% show small velocity values but still demonstrate movement trends. The remaining 30% are relatively stable. Similar clustering results were obtained from the three datasets using unsupervised methods, highlighting the effectiveness of identifying spatial–temporal patterns over the study area. Moreover, It was found that clustering based on kinematic characteristics enhances the interpretation of InSAR deformation, particularly for points with small deformation velocities. Finally, the significance of PCA decomposition in interpreting InSAR deformation was discussed, as it can better represent series with noise, enabling their accurate identification. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring II)
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28 pages, 23537 KiB  
Article
Airborne Short-Baseline Millimeter Wave InSAR System Analysis and Experimental Results
by Luhao Wang, Yabo Liu, Qingxin Chen, Xiaojie Zhou, Shuang Zhu and Shilong Chen
Remote Sens. 2024, 16(6), 1020; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16061020 - 13 Mar 2024
Viewed by 606
Abstract
For the challenges of high-precision mapping in complex terrain, a novel airborne Interferometric Synthetic Aperture Radar (InSAR) system is designed. This system, named ASMIS (Airborne Short-Baseline Millimeter-Wave InSAR System), adopts the coplanar antenna and a pod-type structure. This design makes the system lightweight [...] Read more.
For the challenges of high-precision mapping in complex terrain, a novel airborne Interferometric Synthetic Aperture Radar (InSAR) system is designed. This system, named ASMIS (Airborne Short-Baseline Millimeter-Wave InSAR System), adopts the coplanar antenna and a pod-type structure. This design makes the system lightweight and highly integrated. It can be compatible with small general aviation flight platforms. The baseline is millimeters in size, which greatly simplifies the unwrapping process. The coplanar antennas have two advantages: they maximize the baseline utilization and minimize the Doppler decorrelation and the motion error inconsistency. Acquisition campaigns of the system have been carried out in Boao, Bayannur, and Chengde, China. In the Chengde experimental area, we designed an antiparallel flight experiment to account for the topographic relief. High-precision Digital Orthophoto Maps (DOMs) and Digital Surface Models (DSMs) at a scale of 1:5000 were obtained. The coordinate Root Mean Square Error (RMSE) of the checkpoints within the obtained DSM is less than 0.82 m in altitude and 3 m horizontally. The RMSE of the Sparse Ground Control Points (GCPs) within the obtained DSM is less than 0.3 m in altitude. Experimental results from different areas, including plains, mountains, and coastlines, demonstrate the system’s performance. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring II)
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20 pages, 9039 KiB  
Article
A Spatial–Spectral Transformer for Hyperspectral Image Classification Based on Global Dependencies of Multi-Scale Features
by Yunxuan Ma, Yan Lan, Yakun Xie, Lanxin Yu, Chen Chen, Yusong Wu and Xiaoai Dai
Remote Sens. 2024, 16(2), 404; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16020404 - 20 Jan 2024
Viewed by 922
Abstract
Vision transformers (ViTs) are increasingly utilized for HSI classification due to their outstanding performance. However, ViTs encounter challenges in capturing global dependencies among objects of varying sizes, and fail to effectively exploit the spatial–spectral information inherent in HSI. In response to this limitation, [...] Read more.
Vision transformers (ViTs) are increasingly utilized for HSI classification due to their outstanding performance. However, ViTs encounter challenges in capturing global dependencies among objects of varying sizes, and fail to effectively exploit the spatial–spectral information inherent in HSI. In response to this limitation, we propose a novel solution: the multi-scale spatial–spectral transformer (MSST). Within the MSST framework, we introduce a spatial–spectral token generator (SSTG) and a token fusion self-attention (TFSA) module. Serving as the feature extractor for the MSST, the SSTG incorporates a dual-branch multi-dimensional convolutional structure, enabling the extraction of semantic characteristics that encompass spatial–spectral information from HSI and subsequently tokenizing them. TFSA is a multi-head attention module with the ability to encode attention to features across various scales. We integrated TFSA with cross-covariance attention (CCA) to construct the transformer encoder (TE) for the MSST. Utilizing this TE to perform attention modeling on tokens derived from the SSTG, the network effectively simulates global dependencies among multi-scale features in the data, concurrently making optimal use of spatial–spectral information in HSI. Finally, the output of the TE is fed into a linear mapping layer to obtain the classification results. Experiments conducted on three popular public datasets demonstrate that the MSST method achieved higher classification accuracy compared to state-of-the-art (SOTA) methods. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring II)
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19 pages, 18761 KiB  
Article
The Prediction of Cross-Regional Landslide Susceptibility Based on Pixel Transfer Learning
by Xiao Wang, Di Wang, Xinyue Li, Mengmeng Zhang, Sizhi Cheng, Shaoda Li, Jianhui Dong, Luting Xu, Tiegang Sun, Weile Li, Peilian Ran, Liang Liu, Baojie Wang, Ling Zhao and Xinyi Huang
Remote Sens. 2024, 16(2), 347; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16020347 - 15 Jan 2024
Cited by 1 | Viewed by 690
Abstract
Considering the great time and labor consumption involved in conventional hazard assessment methods in compiling landslide inventory, the construction of a transferable landslide susceptibility prediction model is crucial. This study employs UAV images as data sources to interpret the typical alpine valley area [...] Read more.
Considering the great time and labor consumption involved in conventional hazard assessment methods in compiling landslide inventory, the construction of a transferable landslide susceptibility prediction model is crucial. This study employs UAV images as data sources to interpret the typical alpine valley area of Beichuan County. Eight environmental factors including a digital elevation model (DEM) are extracted to establish a pixel-wise dataset, along with interpreted landslide data. Two landslide susceptibility models were built, each with a deep neural network (DNN) and a support vector machine (SVM) as the learner, and the DNN model was determined to have the best pre-training performance (accuracy = 88.6%, precision = 91.3%, recall = 94.8%, specificity = 87.8%, F1-score = 93.0%, and area under curve = 0.943), with higher parameters in comparison to the SVM model (accuracy = 77.1%, precision = 80.9%, recall = 87.8%, specificity = 73.9%, F1-score = 84.2%, and area under curve = 0.878). The susceptibility model of Beichuan County is then transferred to Mao County (which has no available dataset) to realize cross-regional landslide susceptibility prediction. The results suggest that the model predictions accomplish susceptibility zoning principles and that the DNN model can more precisely distinguish between high and very-high susceptibility areas in relation to the SVM model. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring II)
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19 pages, 17657 KiB  
Article
Oilfield Reservoir Parameter Inversion Based on 2D Ground Deformation Measurements Acquired by a Time-Series MSBAS-InSAR Method
by Anmengyun Liu, Rui Zhang, Yunjie Yang, Tianyu Wang, Ting Wang, Age Shama, Runqing Zhan and Xin Bao
Remote Sens. 2024, 16(1), 154; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16010154 - 30 Dec 2023
Viewed by 764
Abstract
Time-series ground deformation monitoring and reservoir parameter inversion are crucial for the dynamic assessment of oilfield resources and sustainable exploitation in oilfields. As some of the regions with the richest oil reserves in China, the oilfield areas in the western Qaidam Basin were [...] Read more.
Time-series ground deformation monitoring and reservoir parameter inversion are crucial for the dynamic assessment of oilfield resources and sustainable exploitation in oilfields. As some of the regions with the richest oil reserves in China, the oilfield areas in the western Qaidam Basin were selected as a typical study area. Firstly, we used SAR images collected by the Sentinel-1A satellite from January 2021 to December 2022 and applied the multidimensional small baseline subset (MSBAS) method to obtain vertical and east–west deformation measurements. On this basis, a nonlinear Bayesian inversion method was applied to model the shallow reservoir in a series of complex deformation areas, based on a single-source model and a multi-source model, respectively. As a result, the ground deformation monitoring results obtained by long time-series InSAR clearly reflect the uneven ground deformation caused by the oil extraction and water injection operation processes. There was slight subsidence in the Huatugou oilfield, while significant uplift deformation occurred in the Ganchaigou oilfield and the Youshashan oilfield, with a maximum uplift rate of 48 mm/year. Further analysis indicated that the introduction of the 2D deformation field helps to improve the robustness of oilfield reservoir parameter inversion. Moreover, the dual-source model is more suitable than the single-source model for inverting reservoir parameters of complex deformation. This study not only fills the gap of InSAR deformation monitoring for the oilfields in the western Qaidam Basin but also provides a theoretical reference for the model and method selection of reservoir parameter inversion in other oilfields. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring II)
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15 pages, 19638 KiB  
Technical Note
Correcting the Location Error of Persistent Scatterers in an Urban Area Based on Adaptive Building Contours Matching: A Case Study of Changsha
by Miaowen Hu, Bing Xu, Jia Wei, Bangwei Zuo, Yunce Su and Yirui Zeng
Remote Sens. 2024, 16(9), 1543; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16091543 - 26 Apr 2024
Viewed by 117
Abstract
Persistent Scatterer InSAR (PS-InSAR) technology enables the monitoring of displacement in millimeters. However, without the use of external parameter correction, radar scatterers exhibit poor geopositioning precision in meters, limiting the correlation between observed deformation and the actual structure. The integration of PS-InSAR datasets [...] Read more.
Persistent Scatterer InSAR (PS-InSAR) technology enables the monitoring of displacement in millimeters. However, without the use of external parameter correction, radar scatterers exhibit poor geopositioning precision in meters, limiting the correlation between observed deformation and the actual structure. The integration of PS-InSAR datasets and building databases is often overlooked in deformation research. This paper presents a novel strategy for matching between PS points and building contours based on spatial distribution characteristics. A convex hull is employed to simplify the building outline. Considering the influence of building height and incident angle on geometric distortion, an adaptive buffer zone is established. The PS points on a building are further identified through the nearest neighbor method. In this study, both ascending and descending TerraSAR-X orbit datasets acquired between 2016 and 2019 were utilized for PS-InSAR monitoring. The efficacy of the proposed method was evaluated by comparing the PS-InSAR results obtained from different orbits. Through a process of comparison and verification, it was demonstrated that the matching effect between PS points and building contours was significantly enhanced, resulting in an increase of 29.2% in the number of matching PS points. The results indicate that this novel strategy can be employed to associate PS points with building outlines without the need for complex calculations, thereby providing a robust foundation for subsequent building risk assessment. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring II)
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