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Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II
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Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2216

Special Issue Editors

Dr. Fang Shang

E-Mail Website
Guest Editor
Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo 1828585, Japan
Interests: radar polarimetry; synthetic aperture radar; radar imaging; image processing; neural networks
Special Issues, Collections and Topics in MDPI journals
Dr. Lamei Zhang

E-Mail Website
Guest Editor
Department of Information Engineering, Harbin Institute of Technology, 92 Xidazhi St, Nangang, Harbin 150006, China
Interests: radar polarimetry; synthetic aperture radar; image processing; SAR Intelligent Interpretation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue, “Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application”, a new one has been opened for submissions.

Over the last decade, the study of human–environment relationships has demonstrated further importance, as we need to ask how to utilize and protect the natural world, how to build and optimize infrastructure, and how to prepare and respond to disasters in a better way. In order to find the answers, we usually need to collect global, continuous, and/or precise environmental information using remote sensing methods. Synthetic aperture radar (SAR) is known for its imaging potential in situations where darkness, clouds, or smoke would obscure the view of optical sensors, and so it is highly useful for environmental observing. Nowadays, SAR scientific and technical innovations in calibration, information extraction, new imaging techniques, and algorithm adjusting for various specific applications are required.

This Special Issue aims to publish studies covering almost all topics related to SAR. Hence, studies are welcome that focus on the basic theory, calibration, data processing, image interpretation (such as with decomposition algorithms), and various applications of SAR. Articles may address, but are not limited, to the following topics:

  • Calibration of SAR data;
  • SAR applications;
  • Present and future SAR systems and missions;
  • Electromagnetic modeling;
  • InSAR and high-resolution SAR;
  • POL and POLInSAR;
  • Bistatic SAR;
  • SAR/GMTI/STAP and change detection;
  • Image filtering, correction and enhancement;
  • SAR/ISAR signal processing;
  • Advanced and innovative SAR concepts and modes;
  • Artificial intelligence algorithms and their applications in SAR.

Dr. Fang Shang
Dr. Lamei Zhang
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

  • synthetic aperture radar
  • PolSAR
  • InSAR
  • POLInSAR
  • calibration
  • signal processing
  • SAR applications
  • SAR intelligent interpretation

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Related Special Issue

Published Papers (3 papers)

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Research

20 pages, 19115 KiB  
Article
Correction of Ionospheric Phase in SAR Interferometry Considering Wavenumber Shift
by Gen Li, Zihan Hu, Yifan Wang, Zehua Dong and Han Li
Remote Sens. 2024, 16(14), 2555; https://doi.org/10.3390/rs16142555 - 12 Jul 2024
Viewed by 521
Abstract
The ionospheric effects in repeat-pass SAR interferometry (InSAR) have become a rising concern with the increasing interest in low-frequency SAR. The ionosphere will introduce serious phase errors in the interferogram, which should be properly corrected. In this paper, the influence of the wavenumber [...] Read more.
The ionospheric effects in repeat-pass SAR interferometry (InSAR) have become a rising concern with the increasing interest in low-frequency SAR. The ionosphere will introduce serious phase errors in the interferogram, which should be properly corrected. In this paper, the influence of the wavenumber shift on the Range Split-Spectrum (RSS) method is analyzed quantitatively. It is shown that the split-spectrum processing deteriorates the coherence of the sub-band interferogram and then greatly reduces the estimation accuracy. The RSS method combined with common band filtering (CBF) can improve the coherence of sub-band interferograms and estimation accuracy, but the estimation is biased due to the RSS model mismatch. To address the problem, a modified truncated singular value decomposition (MTSVD) based multi-sub-band RSS method is proposed in this paper. The proposed method divides the range common spectrum into multiple sub-bands to jointly estimate the ionospheric phase. The performance of the proposed method is analyzed and validated based on simulation experiments. The results show that the proposed method has stronger robustness and higher accuracy. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)
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13 pages, 1001 KiB  
Communication
A Power Combiner–Splitter Based on a Rat-Race Coupler for an IQ Mixer in Synthetic Aperture Radar Applications
by Abdurrasyid Ruhiyat, Farohaji Kurniawan and Catur Apriono
Remote Sens. 2024, 16(13), 2386; https://doi.org/10.3390/rs16132386 - 28 Jun 2024
Viewed by 438
Abstract
Synthetic aperture radar (SAR) is a powerful tool in remote sensing applications that can produce high-resolution images and operate in any weather condition. It is composed of many RF components, such as the IQ mixer, which mixes the base chirp signal (IF) with [...] Read more.
Synthetic aperture radar (SAR) is a powerful tool in remote sensing applications that can produce high-resolution images and operate in any weather condition. It is composed of many RF components, such as the IQ mixer, which mixes the base chirp signal (IF) with the carrier signal (LO) and increases the bandwidth of the transmitted signal to twice the maximum frequency of the base chirp signal, reducing the workload of Programmable Field Gate Arrays (FPGA) and increasing the resolution of the SAR system. This research proposes a power combiner–splitter design that will be used as a supporting component to construct the IQ mixer in SAR applications based on a rat-race coupler. The measurement results show that the coupler has good S-parameter values. S11, S22, and S33 have a low reflection value below −17 dB, S13 has a high isolation value below −22 dB, and S21 and S31 have a low attenuation value below −4 dB with amplitude unbalance below 0.1 dB and phase unbalance below 1°. The 150 MHz requirement bandwidth for the RF signal is also achieved. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)
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16 pages, 6420 KiB  
Article
Near Real-Time Monitoring of Large Gradient Nonlinear Subsidence in Mining Areas: A Hybrid SBAS-InSAR Method Integrating Robust Sequential Adjustment and Deep Learning
by Yuanjian Wang, Ximin Cui, Yuhang Che, Yuling Zhao, Peixian Li, Xinliang Kang and Yue Jiang
Remote Sens. 2024, 16(10), 1664; https://doi.org/10.3390/rs16101664 - 8 May 2024
Viewed by 814
Abstract
With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series [...] Read more.
With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)
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