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Advancing Synthetic Aperture Radar: Imaging, Processing, and Applications in Remote Sensing

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1617

Special Issue Editors

Dr. Shiqi Xing

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Guest Editor
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: SAR imaging; SAR countermeasure; SAR signal processing
Dr. Sinong Quan

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Guest Editor
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: SAR/PolSAR information processing
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Zhang

E-Mail
Guest Editor
School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: SAR/PolSAR image processing; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synthetic Aperture Radar (SAR), with its all-day, all-weather, and global observation capabilities, has become an indispensable device for Earth observation across diverse environmental and operational conditions. Currently, SAR has achieved significant technological breakthroughs in the following critical aspects: resolution enhancement from meter-level to submeter-level, imaging mode evolution from conventional stripmap mode to advanced azimuth beam steering techniques like spotlight mode, system expansion from single-channel to multi-channel architectures, and polarization advancement from basic single-polarization to comprehensive full-polarization measurements. With continuous technological progress in sensor design and information processing, future SAR will achieve further breakthroughs in innovative imaging concepts, advanced technical approaches, and novel imaging modes. These advancements specifically include high-resolution wide-swath imaging solutions that overcome traditional resolution-swath tradeoffs, multi-static SAR configurations with distributed platforms, payload and intelligence-embedded systems incorporating AI-driven processing.

Without doubt, these technologies will expand SAR's observational dimensions across spatial, temporal, and polarization domains, ultimately enabling comprehensive multidimensional information acquisition. Given these, it is necessary and meaningful to set one Special Issue for further stimulating the development of next-generation SAR applications.

This Special Issue aims at studies covering multi-mode SAR imaging and information processing in remote sensing. The integration of advanced SAR processing techniques—such as signal processing, image processing, and machine learning—is essential to enhance SAR image interpretation and extract meaningful geophysical parameters. The synergy between SAR imaging and information processing drives innovation in remote sensing, addressing challenges in data analytics and automated decision-making for sustainable development.

To promote and guide the further development and application of SAR technology, we are now calling papers for the Special Issue, and warmly welcome contributions from the experts and the scholars all over the world.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

(1) Multi-mode SAR Signal Processing;

(2) SAR Imaging Jamming and Anti-jamming;

(3) Target Scattering Characteristic Analysis for SAR Images;

(4) SAR Image Feature Modulation;

(5) SAR Target Detection and Recognition;

(6) AI-based SAR Information Processing;

(7) SAR/PolSAR Image Processing;

(8) Digital Beamforming.

Dr. Shiqi Xing
Dr. Sinong Quan
Dr. Tao 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 imaging
  • SAR information processing
  • SAR signal processing
  • SAR image processing
  • digital beamforming

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Published Papers (4 papers)

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Research

21 pages, 31171 KiB  
Article
Local Information-Driven Hierarchical Fusion of SAR and Visible Images via Refined Modal Salient Features
by Yunzhong Yan, La Jiang, Jun Li, Shuowei Liu and Zhen Liu
Remote Sens. 2025, 17(14), 2466; https://doi.org/10.3390/rs17142466 - 16 Jul 2025
Viewed by 130
Abstract
Compared to other multi-source image fusion tasks, visible and SAR image fusion faces a lack of training data in deep learning-based methods. Introducing structural priors to design fusion networks is a viable solution. We incorporated the feature hierarchy concept from computer vision, dividing [...] Read more.
Compared to other multi-source image fusion tasks, visible and SAR image fusion faces a lack of training data in deep learning-based methods. Introducing structural priors to design fusion networks is a viable solution. We incorporated the feature hierarchy concept from computer vision, dividing deep features into low-, mid-, and high-level tiers. Based on the complementary modal characteristics of SAR and visible, we designed a fusion architecture that fully analyze and utilize the difference of hierarchical features. Specifically, our framework has two stages. In the cross-modal enhancement stage, a CycleGAN generator-based method for cross-modal interaction and input data enhancement is employed to generate pseudo-modal images. In the fusion stage, we have three innovations: (1) We designed feature extraction branches and fusion strategies differently for each level based on the features of different levels and the complementary modal features of SAR and visible to fully utilize cross-modal complementary features. (2) We proposed the Layered Strictly Nested Framework (LSNF), which emphasizes hierarchical differences and uses hierarchical characteristics, to reduce feature redundancy. (3) Based on visual saliency theory, we proposed a Gradient-weighted Pixel Loss (GWPL), which dynamically assigns higher weights to regions with significant gradient magnitudes, emphasizing high-frequency detail preservation during fusion. Experiments on the YYX-OPT-SAR and WHU-OPT-SAR datasets show that our method outperforms 11 state-of-the-art methods. Ablation studies confirm each component’s contribution. This framework effectively meets remote sensing applications’ high-precision image fusion needs. Full article
(This article belongs to the Special Issue Advancing Synthetic Aperture Radar: Imaging, Processing, and Applications in Remote Sensing)
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24 pages, 4085 KiB  
Article
A Joint Optimization Method for Power and Array of Multi-Point Sources System
by Zhihao Cai, Shiqi Xing, Xinyuan Su, Junpeng Wang, Weize Meng and Ziwen Xiao
Remote Sens. 2025, 17(14), 2445; https://doi.org/10.3390/rs17142445 - 14 Jul 2025
Viewed by 190
Abstract
In a multi-point source system, increasing the jamming power can expand the distribution area of the equivalent radiation center, but significantly increases the system exposure risk. Therefore, in order to achieve an optimal balance between the two, this paper proposes a joint optimization [...] Read more.
In a multi-point source system, increasing the jamming power can expand the distribution area of the equivalent radiation center, but significantly increases the system exposure risk. Therefore, in order to achieve an optimal balance between the two, this paper proposes a joint optimization method for jamming power and an array of multi-point source systems. First, based on determining the spatial geometric relationship between the triplet antenna and the target, the distribution law of the equivalent radiation center of the triplet antenna under the condition of the target echo is derived. Second, by introducing the angle factor, the jamming power and equivalent radiation center distribution area are combined to construct the joint optimization model of jamming power and array in omnidirectional and non-omnidirectional situations. Third, based on the non-dominated sorting whale optimization algorithm (NSWOA), an adaptive inertia weight based on the cosine function and logistic chaotic map is introduced to obtain the optimal arrangement. The experimental results show that in the omnidirectional case, when the average jamming-to-signal ratio is 13.83 dB, the equilateral triangle array can achieve the goal of protecting the target while avoiding the exposure of the triplet antenna position. In the non-omnidirectional case, when the average jamming-to-signal ratio is 13.90 dB, the equilateral triangle array can achieve the optimal balance between the jamming power and the area of the distribution area of the equivalent radiation center, and control the distribution of the equivalent radiation center to strictly meet the preset angular domain constraints. Furthermore, the optimal JSR value was reduced by an average of 1.14 dB compared with that of the conventional selection scheme. Full article
(This article belongs to the Special Issue Advancing Synthetic Aperture Radar: Imaging, Processing, and Applications in Remote Sensing)
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31 pages, 18652 KiB  
Article
Improved Real-Time SPGA Algorithm and Hardware Processing Architecture for Small UAVs
by Huan Wang, Yunlong Liu, Yanlei Li, Hang Li, Xuyang Ge, Jihao Xin and Xingdong Liang
Remote Sens. 2025, 17(13), 2232; https://doi.org/10.3390/rs17132232 - 29 Jun 2025
Viewed by 333
Abstract
Real-time Synthetic Aperture Radar (SAR) imaging for small Unmanned Aerial Vehicles (UAVs) has become a significant research focus. However, limitations in Size, Weight, and Power (SwaP) restrict the imaging quality and timeliness of small UAV-borne SAR, limiting its practical application. This paper presents [...] Read more.
Real-time Synthetic Aperture Radar (SAR) imaging for small Unmanned Aerial Vehicles (UAVs) has become a significant research focus. However, limitations in Size, Weight, and Power (SwaP) restrict the imaging quality and timeliness of small UAV-borne SAR, limiting its practical application. This paper presents a non-iterative real-time Feature Sub-image Based Stripmap Phase Gradient Autofocus (FSI-SPGA) algorithm. The FSI-SPGA algorithm combines 2D Constant False Alarm Rate (CFAR) for coarse point selection and spatial decorrelation for refined point selection. This approach enables the accurate extraction of high-quality scattering points. Using these points, the algorithm constructs a feature sub-image containing comprehensive phase error information and performs a non-iterative phase error estimation based on this sub-image. To address the multifunctional, low-power, and real-time requirements of small UAV SAR, we designed a highly efficient hybrid architecture. This architecture integrates dataflow reconfigurability and dynamic partial reconfiguration and is based on an ARM + FPGA platform. It is specifically tailored to the computational characteristics of the FSI-SPGA algorithm. The proposed scheme was assessed using data from a 6 kg small SAR system equipped with centimeter-level INS/GPS. For SAR images of size 4096 × 12,288, the FSI-SPGA algorithm demonstrated a 6 times improvement in processing efficiency compared to traditional methods while maintaining the same level of precision. The high-efficiency reconfigurable ARM + FPGA architecture processed the algorithm in 6.02 s, achieving 12 times the processing speed and three times the energy efficiency of a single low-power ARM platform. These results confirm the effectiveness of the proposed solution for enabling high-quality real-time SAR imaging under stringent SwaP constraints. Full article
(This article belongs to the Special Issue Advancing Synthetic Aperture Radar: Imaging, Processing, and Applications in Remote Sensing)
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28 pages, 9711 KiB  
Article
Analyzing the Adversarial Robustness and Interpretability of Deep SAR Classification Models: A Comprehensive Examination of Their Reliability
by Tianrui Chen, Limeng Zhang, Weiwei Guo, Zenghui Zhang and Mihai Datcu
Remote Sens. 2025, 17(11), 1943; https://doi.org/10.3390/rs17111943 - 4 Jun 2025
Viewed by 560
Abstract
Deep neural networks (DNNs) have shown strong performance in synthetic aperture radar (SAR) image classification. However, their “black-box” nature limits interpretability and poses challenges for robustness, which is critical for sensitive applications such as disaster assessment, environmental monitoring, and agricultural insurance. This study [...] Read more.
Deep neural networks (DNNs) have shown strong performance in synthetic aperture radar (SAR) image classification. However, their “black-box” nature limits interpretability and poses challenges for robustness, which is critical for sensitive applications such as disaster assessment, environmental monitoring, and agricultural insurance. This study systematically evaluates the adversarial robustness of five representative DNNs (VGG11/16, ResNet18/101, and A-ConvNet) under a variety of attack and defense settings. Using eXplainable AI (XAI) techniques and attribution-based visualizations, we analyze how adversarial perturbations and adversarial training affect model behavior and decision logic. Our results reveal significant robustness differences across architectures, highlight interpretability limitations, and suggest practical guidelines for building more robust SAR classification systems. We also discuss challenges associated with large-scale, multi-class land use and land cover (LULC) classification under adversarial conditions. Full article
(This article belongs to the Special Issue Advancing Synthetic Aperture Radar: Imaging, Processing, and Applications in Remote Sensing)
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