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Advances in Synthetic Aperture Radar Data Processing and Application (Second Edition)

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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 4944

Special Issue Editors

Prof. Dr. Hui Bi

E-Mail Website
Guest Editor
Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: radar imaging; synthetic aperture radar; remote sensing by radar; convolutional neural nets; image classification
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Daiyin Zhu

E-Mail Website
Guest Editor
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: radar signal processing; high-resolution SAR imaging method
Special Issues, Collections and Topics in MDPI journals
Dr. Jingjing Zhang

E-Mail Website
Guest Editor
College of Electronic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: radar singal processing; radar system design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the second edition of the Special Issue entitled “Advances in Synthetic Aperture Radar Data Processing and Application”. After the resounding success of our first edition, we are thrilled to launch the second edition.

Synthetic aperture radar (SAR) is an active high-resolution microwave imaging technique. Compared with the typical optical system, it has all-time and all-weather surveillance capabilities and is therefore widely employed in military, mapping, agriculture, and disaster monitoring applications. In recent years, the development of SAR has advanced rapidly. Additional SAR satellites have been launched, providing rich data support for its application in many fields. In addition, due to the advantages of UAV, such as its low cost, easy and rapid deployment, and miniaturization, the application of UAV-borne SAR has also advanced rapidly; it now plays an increasingly vital role in applications such as reconnaissance and mapping.

The main objective of this Special Issue is to provide a platform for communication regarding the latest advanced SAR data processing technology and applications, so that researchers can obtain a clear understanding of the development of this field. This Special Issue aims to provide a comprehensive overview of the state-of-the-art technologies currently employed in the processing and application of SAR data.

The scope of this Special Issue includes, but is not limited to, the following topics related to SAR data processing:

  • High-resolution/wide-swath/squint/multi-aspect/multi-frequency SAR imaging
  • SAR image generation, enhancement, motion compensation, autofocusing
  • 3D/4D SAR imaging (Tomography, D-Tomography, Holography, etc)
  • ISAR data processing
  • Moving target imaging
  • Advanced SAR data processing techniques
  • SAR data and image-based urban, land, ocean, ice, soil and vegetation applications
  • Disaster monitoring
  • Other applications

Prof. Dr. Hui Bi
Prof. Dr. Daiyin Zhu
Dr. Jingjing 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 (SAR)
  • SAR data processing
  • 3D/4D SAR imaging
  • ISAR imaging
  • moving target imaging
  • SAR applications

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Related Special Issue

Published Papers (6 papers)

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22 pages, 6875 KiB  
Article
A Near-Real-Time Imaging Algorithm for Focusing Spaceborne SAR Data in Multiple Modes Based on an Embedded GPU
by Yunju Zhang, Mingyang Shang, Yini Lv and Xiaolan Qiu
Remote Sens. 2025, 17(9), 1495; https://doi.org/10.3390/rs17091495 - 23 Apr 2025
Viewed by 122
Abstract
To achieve on-board real-time processing for sliding-spotlight mode synthetic aperture radar (SAR), on the one hand, this paper proposes an adaptive and efficient imaging algorithm for the sliding-spotlight mode. On the other hand, a batch processing method was designed and optimized based on [...] Read more.
To achieve on-board real-time processing for sliding-spotlight mode synthetic aperture radar (SAR), on the one hand, this paper proposes an adaptive and efficient imaging algorithm for the sliding-spotlight mode. On the other hand, a batch processing method was designed and optimized based on the AGX Orin platform to implement the algorithm effectively. Based on the chirp scaling (CS) algorithm, sliding-spotlight mode imaging can be achieved by adding Deramp preprocessing along with either zero-padding or performing an extra chirp scaling operation. This article analyzes the computational complexity of the two algorithms and provides a criterion called the Method Choice Indicator (MCI) for selecting the appropriate method. Additionally, the mathematical expressions for time–frequency transformation are derived, providing the theoretical basis for calculating the equivalent PRF and the azimuth width represented by a single pixel. To increase the size of the data that AGX Orin can process, the batch processing method was proposed to reduce peak memory usage during imaging, so that the limited memory could be better utilized. Meanwhile, this algorithm was also compatible with strip mode and TOPSAR (Terrain Observation by Progressive scans SAR) mode imaging. While batch processing increased data transfers, the integrated architecture of AGX Orin minimized the negative impact. Subsequently, through a series of optimizations of the algorithm, the efficiency of the algorithm was further improved. As a result, it took 19.25 s to complete the imaging process for sliding-spotlight mode data with a size of 42,966 × 27,648. Since satellite data acquisition time was 11.43 s, it can be considered that this method achieved near-real-time imaging. The experimental results demonstrate the feasibility of on-board processing. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application (Second Edition))
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26 pages, 2105 KiB  
Article
Hybrid Deterministic Sensing Matrix for Compressed Drone SAR Imaging and Efficient Reconstruction of Subsurface Targets
by Hwi-Jeong Jo, Heewoo Lee, Jihoon Choi and Wookyung Lee
Remote Sens. 2025, 17(4), 595; https://doi.org/10.3390/rs17040595 - 10 Feb 2025
Viewed by 594
Abstract
Drone-based synthetic aperture radar (SAR) systems have increasingly gained attention due to their potential for rapid surveillance in localized areas. This paper presents a novel approach to SAR processing for subsurface target detection from a lightweight drone platform. The limited processing capacity and [...] Read more.
Drone-based synthetic aperture radar (SAR) systems have increasingly gained attention due to their potential for rapid surveillance in localized areas. This paper presents a novel approach to SAR processing for subsurface target detection from a lightweight drone platform. The limited processing capacity and memory resources of small SAR platforms demand efficient recovery performance for high-resolution imaging. Compressed sensing (CS) algorithms are widely used to mitigate data storage requirements, yet they often suffer from challenges related to computational burden and detection errors. CS theory exploits signal sparsity and the incoherence of sensing matrices to reconstruct target information from reduced data measurements. Although random sensing matrices are commonly employed to ensure the independence of measured data, they incur high computational cost and memory resources. While deterministic sensing matrices provide fast data recovery, they suffer from increased internal interference, leading to degraded performance in noisy environments. This paper proposes a novel hybrid sensing matrix and recovery algorithm for efficient target detection in small drone-based SAR platforms. After establishing the principles of signal sampling and recovery, SAR imaging simulations are conducted to evaluate the performance of the proposed method with respect to data compression, processing speed, and recovery accuracy. For verification, a custom-built drone SAR platform is utilized to recover subsurface targets obscured by high-clutter backgrounds. Experimental results demonstrate the effective recovery of buried target images, highlighting the potential of the proposed method for practical applications in high-clutter environments. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application (Second Edition))
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26 pages, 5914 KiB  
Article
A Structurally Flexible Occupancy Network for 3-D Target Reconstruction Using 2-D SAR Images
by Lingjuan Yu, Jianlong Liu, Miaomiao Liang, Xiangchun Yu, Xiaochun Xie, Hui Bi and Wen Hong
Remote Sens. 2025, 17(2), 347; https://doi.org/10.3390/rs17020347 - 20 Jan 2025
Cited by 1 | Viewed by 1218
Abstract
Driven by deep learning, three-dimensional (3-D) target reconstruction from two-dimensional (2-D) synthetic aperture radar (SAR) images has been developed. However, there is still room for improvement in the reconstruction quality. In this paper, we propose a structurally flexible occupancy network (SFONet) to achieve [...] Read more.
Driven by deep learning, three-dimensional (3-D) target reconstruction from two-dimensional (2-D) synthetic aperture radar (SAR) images has been developed. However, there is still room for improvement in the reconstruction quality. In this paper, we propose a structurally flexible occupancy network (SFONet) to achieve high-quality reconstruction of a 3-D target using one or more 2-D SAR images. The SFONet consists of a basic network and a pluggable module that allows it to switch between two input modes: one azimuthal image and multiple azimuthal images. Furthermore, the pluggable module is designed to include a complex-valued (CV) long short-term memory (LSTM) submodule and a CV attention submodule, where the former extracts structural features of the target from multiple azimuthal SAR images, and the latter fuses these features. When two input modes coexist, we also propose a two-stage training strategy. The basic network is trained in the first stage using one azimuthal SAR image as the input. In the second stage, the basic network trained in the first stage is fixed, and only the pluggable module is trained using multiple azimuthal SAR images as the input. Finally, we construct an experimental dataset containing 2-D SAR images and 3-D ground truth by utilizing the publicly available Gotcha echo dataset. Experimental results show that once the SFONet is trained, a 3-D target can be reconstructed using one or more azimuthal images, exhibiting higher quality than other deep learning-based 3-D reconstruction methods. Moreover, when the composition of a training sample is reasonable, the number of samples required for the SFONet training can be reduced. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application (Second Edition))
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19 pages, 23424 KiB  
Article
A Multi-Parameter Calibration Method Based on the Newton Method and the Genetic Algorithm in Airborne Array Synthetic Aperture Radar
by Dawei Wang, Zhenhua Li, Fubo Zhang and Longyong Chen
Remote Sens. 2024, 16(24), 4677; https://doi.org/10.3390/rs16244677 - 15 Dec 2024
Viewed by 906
Abstract
Airborne array synthetic aperture radar (SAR) can achieve three-dimensional (3D) imaging of the observed scene in a single flight. Nevertheless, the imaging process of airborne array SAR is subject to various parameter errors due to unstable factors. Such errors degrade the quality of [...] Read more.
Airborne array synthetic aperture radar (SAR) can achieve three-dimensional (3D) imaging of the observed scene in a single flight. Nevertheless, the imaging process of airborne array SAR is subject to various parameter errors due to unstable factors. Such errors degrade the quality of 3D imaging, particularly for the elevation imaging results, which necessitates the employment of super-resolution algorithms. The most significant error parameters include the amplitude and phase imbalances between multiple channels, as well as the phase-center positions of each channel. Owing to the coupled nature of these parameter errors, the calibration accuracy for each parameter independently is relatively sub-par, while super-resolution algorithms have strict demands for parameter precision. Addressing these challenges, this article proposes a multi-parameter calibration method for airborne array SAR based on the Newton method and the genetic algorithm. Initially, a least squares model for multi-parameter calibration is established, followed by leveraging the global optimization characteristics of genetic algorithms and the rapid convergence property of the Newton method. The genetic algorithm is utilized to locate a sub-optimal solution in proximity to the optimal one, subsequently converging swiftly to the optimal solution via the Newton method, which incorporates second-order information. This approach averts the pitfalls of local convergence due to large initial value errors, thereby enhancing the algorithm’s robustness. The proposed method effectively enhances the precision of multi-parameter calibration, which is of significant importance in ensuring the quality of 3D imaging of airborne array SAR. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application (Second Edition))
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19 pages, 15677 KiB  
Article
Automatic Correction of Time-Varying Orbit Errors for Single-Baseline Single-Polarization InSAR Data Based on Block Adjustment Model
by Huacan Hu, Haiqiang Fu, Jianjun Zhu, Zhiwei Liu, Kefu Wu, Dong Zeng, Afang Wan and Feng Wang
Remote Sens. 2024, 16(19), 3578; https://doi.org/10.3390/rs16193578 - 26 Sep 2024
Cited by 2 | Viewed by 1287
Abstract
Orbit error is one of the primary error sources of interferometric synthetic aperture radar (InSAR) and differential InSAR (D-InSAR) measurements, arising from inaccurate orbit determination of SAR platforms. Typically, orbit error in the interferogram can be estimated using polynomial models. However, correcting for [...] Read more.
Orbit error is one of the primary error sources of interferometric synthetic aperture radar (InSAR) and differential InSAR (D-InSAR) measurements, arising from inaccurate orbit determination of SAR platforms. Typically, orbit error in the interferogram can be estimated using polynomial models. However, correcting for orbit errors with significant time-varying characteristics presents two main challenges: (1) the complexity and variability of the azimuth time-varying orbit errors make it difficult to accurately model them using a set of polynomial coefficients; (2) existing patch-based polynomial models rely on empirical segmentation and overlook the time-varying characteristics, resulting in residual orbital error phase. To overcome these problems, this study proposes an automated block adjustment framework for estimating time-varying orbit errors, incorporating the following innovations: (1) the differential interferogram is divided into several blocks along the azimuth direction to model orbit error separately; (2) automated segmentation is achieved by extracting morphological features (i.e., peaks and troughs) from the azimuthal profile; (3) a block adjustment method combining control points and connection points is proposed to determine the model coefficients of each block for the orbital error phase estimation. The feasibility of the proposed method was verified by repeat-pass L-band spaceborne and P-band airborne InSAR data, and finally, the InSAR digital elevation model (DEM) was generated for performance evaluation. Compared with the high-precision light detection and ranging (LiDAR) elevation, the root mean square error (RMSE) of InSAR DEM was reduced from 18.27 m to 7.04 m in the spaceborne dataset and from 7.83~14.97 m to 3.36~6.02 m in the airborne dataset. Then, further analysis demonstrated that the proposed method outperforms existing algorithms under single-baseline and single-polarization conditions. Moreover, the proposed method is applicable to both spaceborne and airborne InSAR data, demonstrating strong versatility and potential for broader applications. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application (Second Edition))
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16 pages, 958 KiB  
Technical Note
Bayesian Time-Domain Ringing Suppression Approach in Impulse Ultrawideband Synthetic Aperture Radar
by Xinhao Xu, Wenjie Li, Haibo Tang, Longyong Chen, Chengwei Zhang, Tao Jiang, Jie Liu and Xingdong Liang
Remote Sens. 2025, 17(8), 1455; https://doi.org/10.3390/rs17081455 - 18 Apr 2025
Viewed by 221
Abstract
Impulse ultrawideband (UWB) synthetic aperture radar (SAR) combines high-azimuth-range resolution with robust penetration capabilities, making it ideal for applications such as through-wall detection and subsurface imaging. In such systems, the performance of UWB antennas is critical for transmitting high-power, large-bandwidth impulse signals. However, [...] Read more.
Impulse ultrawideband (UWB) synthetic aperture radar (SAR) combines high-azimuth-range resolution with robust penetration capabilities, making it ideal for applications such as through-wall detection and subsurface imaging. In such systems, the performance of UWB antennas is critical for transmitting high-power, large-bandwidth impulse signals. However, two primary factors degrade radar imaging quality: (1) inherent limitations in antenna radiation efficiency, which lead to low-frequency signal loss and subsequent time-domain ringing artifacts; (2) impedance mismatch at the antenna terminals, causing standing wave reflections that exacerbate the ringing phenomenon. This study systematically analyzes the mechanisms of ringing generation, including its physical origins and mathematical modeling in SAR systems. Building on this analysis, we propose a Bayesian ringing suppression algorithm based on sparse optimization. The method effectively enhances imaging quality while balancing the trade-off between ringing suppression and image fidelity. Validation through numerical simulations and experimental measurements demonstrates significant suppression of time-domain ringing and improved target clarity. The proposed approach holds critical importance for advancing impulse UWB SAR systems, particularly in scenarios requiring high-resolution imaging. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application (Second Edition))
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