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Applied Sciences
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Latest Advances in Radar Remote Sensing Technologies
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Latest Advances in Radar Remote Sensing Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 7192

Special Issue Editors

Prof. Dr. Yanping Wang

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Guest Editor
School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Interests: ground-based synthetic aperture radar imaging and deformation monitoring
Dr. Wei Pu

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Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: radar imaging; SAR signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xudong Lai

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: SAR data processing and application; LiDAR data processing and application
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xianglei Liu

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Guest Editor
Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Interests: geospatial big data; dynamic monitoring; high-speed videogrammetry
Special Issues, Collections and Topics in MDPI journals
Dr. Jifang Pei

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Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: radar target recognition; radar target detection; radar signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Weibo Huo

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: statistical signal processing; machine learning; radar image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radar remote sensing technology is proven to be helpful in providing important information for the urban and built environment, health of infrastructure and environmental changes, ocean monitoring, land cover dynamics, and so on. In recent decades, with the development of satellite, airborne, and ground-based SAR sensors with high spatial resolution, short revisit days, and multi-polarization, radar remote sensing has been used in a wide range of other applications. For example, urban infrastructure is an important basic condition for the survival and development of a city and is an indispensable part of an urban economy. There is no doubt that the assessment and monitoring of the health status of infrastructure with radar remote sensing technology is essential within the context of the life cycle of structures and their interaction with the environment. Moreover, the emergence of new technologies, such as artificial intelligence, machine learning, and big data, provides further new opportunities for radar remote sensing.

This Special Issue aims to introduce the latest advances in high-resolution SAR/InSAR/PolSAR imaging, high-precision SAR/InSAR/PolSAR target detection and recognition, and urban infrastructure monitoring using radar remote sensing technology. Topics may include high-spatial-resolution SAR/InSAR/PolSAR imaging methods, high-precision SAR/InSAR/PolSAR target detection, and recognition approaches as well as algorithms, applications, mechanism studies, various risk assessments and monitoring methods for urban infrastructure, and so on.

Prof. Dr. Yanping Wang
Dr. Wei Pu
Prof. Dr. Xudong Lai
Prof. Dr. Xianglei Liu
Dr. Jifang Pei
Dr. Weibo Huo
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. Applied Sciences 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 2400 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

  • radar remote sensing
  • infrastructure stability
  • structural health monitoring
  • SAR/InSAR/PolSAR imaging
  • target detection
  • target recognition
  • deep learning
  • land subsidence
  • hardware
  • data sets
  • urban physical examination

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Published Papers (5 papers)

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Research

15 pages, 14003 KiB  
Article
Analysis of the Dihedral Corner Reflector’s RCS Features in Multi-Resource SAR
by Jie Liu, Tao Li, Sijie Ma, Yangmao Wen, Yanhao Xu and Guigen Nie
Appl. Sci. 2024, 14(12), 5054; https://doi.org/10.3390/app14125054 - 10 Jun 2024
Viewed by 461
Abstract
Artificial corner reflectors are widely used in the vegetated landslide for time series InSAR monitoring due to their permanent scattering features. This paper investigated the RCS features of a novel dihedral CR under multi-resource SAR datasets. An RCS reduction model for the novel [...] Read more.
Artificial corner reflectors are widely used in the vegetated landslide for time series InSAR monitoring due to their permanent scattering features. This paper investigated the RCS features of a novel dihedral CR under multi-resource SAR datasets. An RCS reduction model for the novel dihedral corner reflector has been proposed to evaluate the energy loss caused by the deviation between the SAR incident angle and the CR’s axis. On the Huangtupo slope, Badong county, Hubei province, tens of dihedral CRs had been installed and the TSX–spotlight and Sentinel-TOPS data had been collected. Based on the observation results of CRs with more than ten deviation angles, the proposed reduction model was tested with preferable consistency under a real dataset, while 2 dBsm of systematic bias was verified in those datasets. The maximum incident angle deviation in the Sentinel data overlapping area is over 12°, which leads to a 2.4 dBsm RCS decrease for horizontally placed dihedral CRs estimated by the proposed model, which has also been testified by the observed results. The testing results from the Sentinel data show that in high, vegetation-covered mountain areas like the Huangtupo slope, the dihedral CRs with a 0.4 m slide length can be achieve 1 mm precision accuracy, while a side length of 0.2 m can achieve the same accuracy under TSX–spotlight data. Full article
(This article belongs to the Special Issue Latest Advances in Radar Remote Sensing Technologies)
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16 pages, 12972 KiB  
Article
Monte Carlo Sampling of Inverse Problems Based on a Squeeze-and-Excitation Convolutional Neural Network Applied to Ground-Penetrating Radar Crosshole Traveltime: A Numerical Simulation Study
by Hanqing Qiao, Cai Liu and Shengchao Wang
Appl. Sci. 2024, 14(2), 618; https://doi.org/10.3390/app14020618 - 11 Jan 2024
Viewed by 669
Abstract
Monte Carlo-based sampling methods (MCMC) can be used to solve inverse problems affecting ground penetrating radar (GPR) data. However, due to their high computational complexity, they have not been widely used in practical applications. This article uses neural network methods to replace the [...] Read more.
Monte Carlo-based sampling methods (MCMC) can be used to solve inverse problems affecting ground penetrating radar (GPR) data. However, due to their high computational complexity, they have not been widely used in practical applications. This article uses neural network methods to replace the computationally complex forward problem of Monte Carlo methods. However, the neural network method is an approximation of the accurate formula method, and this may introduce model errors. In order to reduce the impact of model errors, in this study, we incorporate the Squeeze-and-Excitation (SE) attention mechanism into Convolutional Neural Networks (CNN) to further improve the accuracy of the network. Moreover, with the statistical advantages of the MCMC method, model errors can be explained during the inversion process, further reducing their impact. We apply the proposed method to solve the inversion problem of crosshole ground-penetrating radar travel time data. Compared with commonly used approximate forward models, the method proposed in this paper has better accuracy. The results of data experiments indicate that this method can effectively invert the velocity of underground media. Full article
(This article belongs to the Special Issue Latest Advances in Radar Remote Sensing Technologies)
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21 pages, 9482 KiB  
Article
Two-Step CFAR-Based 3D Point Cloud Extraction Method for Circular Scanning Ground-Based Synthetic Aperture Radar
by Wenjie Shen, Jie Zhi, Yanping Wang, Jinping Sun, Yun Lin, Yang Li and Wen Jiang
Appl. Sci. 2023, 13(12), 7164; https://doi.org/10.3390/app13127164 - 15 Jun 2023
Cited by 2 | Viewed by 1490
Abstract
Ground-Based Synthetic Aperture Radar (GBSAR) has non-contact, all-weather, high resolution imaging and microdeformation sensing capabilities, which offers advantages in applications such as building structure monitoring and mine slope deformation retrieval. The Circular Scanning Ground-Based Synthetic Aperture Radar (CS-GBSAR) is one of its newest [...] Read more.
Ground-Based Synthetic Aperture Radar (GBSAR) has non-contact, all-weather, high resolution imaging and microdeformation sensing capabilities, which offers advantages in applications such as building structure monitoring and mine slope deformation retrieval. The Circular Scanning Ground-Based Synthetic Aperture Radar (CS-GBSAR) is one of its newest developed working mode, in which the radar rotates around an axis in a vertical plane. Such nonlinear observation geometry brings the unique advantage of three-dimensional (3D) imaging compared with traditional GBSAR modes. However, such nonlinear observation geometry causes strong sidelobes in SAR images, which makes it a difficult task to extract point cloud data. The Conventional Cell Averaging Constant False Alarm Rate (CA-CFAR) algorithm can extract 3D point cloud data layer-by-layer at different heights, which is time consuming and is easily influenced by strong sidelobes to obtain inaccurate results. To address these problems, this paper proposes a new two-step CFAR-based 3D point cloud extraction method for CS-GBSAR, which can extract accurate 3D point cloud data under the influence of strong sidelobes. It first utilizes maximum projection to obtain three-view images from 3D image data. Then, the first step CA-CFAR is applied to obtain the coarse masks of three-views. Then, the volume mask in the original 3D image is obtained via inverse projection. This can remove strong sidelobes outside the potential target region and obtain potential target area data by intersecting it with the SAR 3D image. Then, the second step CA-CFAR is applied to the potential target area data to obtain 3D point clouds. Finally, to further eliminate the residual strong sidelobes and output accurate 3D point clouds, the modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is applied. The original DBSCAN method uses a spherical template to cluster. It covers more points, which is easily influenced by the strong sidelobe. Hence, the clustering results have more noise points. Meanwhile, modified DBSCAN clusters have a cylindrical template to accommodate the data’s features, which can reduce false clustering. The proposed method is validated via real data acquired by the North China University of Technology (NCUT)-developed CS-GBSAR system. The laser detection and ranging (LiDAR) data are used as the reference ground truth to demonstrate the method. The comparison experiment with conventional method shows that the proposed method can reduce 95.4% false clustered points and remove the strong sidelobes, which shows the better performance of the proposed method. Full article
(This article belongs to the Special Issue Latest Advances in Radar Remote Sensing Technologies)
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14 pages, 5999 KiB  
Article
A Lightweight Model for 3D Point Cloud Object Detection
by Ziyi Li, Yang Li, Yanping Wang, Guangda Xie, Hongquan Qu and Zhuoyang Lyu
Appl. Sci. 2023, 13(11), 6754; https://doi.org/10.3390/app13116754 - 1 Jun 2023
Viewed by 2166
Abstract
With the rapid development of deep learning, more and more complex models are applied to 3D point cloud object detection to improve accuracy. In general, the more complex the model, the better the performance and the greater the computational resource consumption it has. [...] Read more.
With the rapid development of deep learning, more and more complex models are applied to 3D point cloud object detection to improve accuracy. In general, the more complex the model, the better the performance and the greater the computational resource consumption it has. However, complex models are incompatible for deployment on edge devices with restricted memory, so accurate and efficient 3D point cloud object detection processing is necessary. Recently, a lightweight model design has been proposed as one type of effective model compression that aims to design more efficient network computing methods. In this paper, a lightweight 3D point cloud object detection network architecture is proposed. The core innovation of the proposal consists of a lightweight 3D sparse convolution layer module (LW-Sconv module) and knowledge distillation loss. Firstly, in the LW-Sconv module, factorized convolution and group convolution are applied to the standard 3D sparse convolution layer. As the basic component of the lightweight 3D point cloud object detection network proposed in this paper, the LW-Sconv module greatly reduces the complexity of the network. Then, the knowledge distillation loss is used to guide the training of the lightweight network proposed in this paper to further improve the detection accuracy. Finally, extensive experiments are performed to verify the algorithm proposed in this paper. Compared with the baseline model, the proposed model can reduce the FLOPs and parameters by 3.7 times and 7.9 times, respectively. The lightweight model trained with knowledge distillation loss achieves comparable accuracy to the baseline. Experiments show that the proposed method greatly reduces the model complexity while ensuring detection accuracy. Full article
(This article belongs to the Special Issue Latest Advances in Radar Remote Sensing Technologies)
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15 pages, 7790 KiB  
Article
Time-Series InSAR Deformation Monitoring of High Fill Characteristic Canal of South–North Water Diversion Project in China
by Hui Liu, Wenfei Zhao, Zhen Qin, Tiesheng Wang, Geshuang Li and Mengyuan Zhu
Appl. Sci. 2023, 13(11), 6415; https://doi.org/10.3390/app13116415 - 24 May 2023
Cited by 3 | Viewed by 1278
Abstract
The Middle Route of the South–North Water Diversion Project has changed the water resources pattern in China. As advanced equipment for the country, it is responsible for the water supply “lifeline” of Beijing, Tianjin, Hebei, Henan, etc. Ensuring its safe operation is a [...] Read more.
The Middle Route of the South–North Water Diversion Project has changed the water resources pattern in China. As advanced equipment for the country, it is responsible for the water supply “lifeline” of Beijing, Tianjin, Hebei, Henan, etc. Ensuring its safe operation is a top priority to promote social stability and coordinated economic development between the North and the South. Used persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to monitor the deformation of the high fill characteristic canal in Wenzhuang Village, Ye County, during the period from October 2016 to June 2017 for the South–North Water Diversion Project showed that there was significant deformation on the 1 km-long slope of the east bank of the canal, with the maximum deformation volume reaching 36 mm. Through the comparison and verification with the second order leveling data, there are more than 87% of the root mean square error of both less than ±2 mm. The correlation coefficient is 0.96, and the two were highly consistent in deformation trends and values. Through the vertical and cross-sectional analysis of the canal’s east bank and four key monitoring sections, it was found that the east bank of the canal presents overall uneven subsidence, and the closer the canal is to the water, the greater the canal deformation, and vice versa. Further comparison of the PS-InSAR deformation results of the canal from October 2016 to February 2018 proves that this technology cannot only monitor the subsidence range and rate of the South–North Water Diversion canal but also accurately identify the subsidence sequence of the east and west banks. It can provide reliable technical support for the safety monitoring and disaster prevention of the South–North Water Diversion canal characterized by high fill and deep excavation. Full article
(This article belongs to the Special Issue Latest Advances in Radar Remote Sensing Technologies)
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