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Synthetic Aperture Radar (SAR) Image Object Detection and Information Extraction: Methods and Applications (Second Edition)

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

Deadline for manuscript submissions: 26 November 2025 | Viewed by 1341

Special Issue Editors

Dr. Kefeng Ji

E-Mail Website
Guest Editor
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: remote sensing information processing; synthetic aperture radar (SAR) image interpretation; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Mingjin Zhang

E-Mail Website
Guest Editor
School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
Interests: SAR image object detection; computer vision; pattern recognition; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangguang Leng

E-Mail Website
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: remote sensing information processing; synthetic aperture radar (SAR) image interpretation; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Haohao Ren

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731,China
Interests: synthetic aperture radar (SAR); image processing; feature extraction; automatic target detection and recognition; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongzhen Sun

E-Mail Website
Guest Editor Assistant
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: SAR image object detection and recognition; AI for SAR ship detection; multi-temporal SAR image interpretation

Special Issue Information

Dear Colleagues,

Synthetic aperture radar (SAR) is famous for its 24-hour all-weather imaging capabilities. In recent years, SAR imaging capabilities have improved dramatically. However, SAR image interpretation still faces great challenges. Object detection and information extraction are among the fundamental tasks, which is of great significance in civilian applications.

This Special Issue aims to collect the advanced methods and applications in SAR image object detection and information extraction. We are pleased to invite you to contribute your newest research results to this Special Issue. In this Special Issue, original research articles and reviews are welcome.

Research areas may include (but are not limited to) the following:

(1) Object detection and information extraction in spaceborne/airborne SAR images;

(2) Object detection and information extraction in miniSAR/nanoSAR images;

(3) Object detection and information extraction based on edge/cloud computing;

(4) Detection and information extraction of moving targets such as ships, aircraft and vehicles;

(5) Information extraction of fixed facilities such as buildings and bridges;

(6) Moving target refocusing and recognition;

(7) Integration of intelligent imaging and recognition;

(8) Multi-modal data-assisted object detection or recognition in SAR images.

Dr. Kefeng Ji
Dr. Mingjin Zhang
Dr. Xiangguang Leng
Dr. Haohao Ren
Guest Editors

Dr. Zhongzhen Sun
Guest Editor Assistant

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)
  • object detection
  • information extraction

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Related Special Issue

Published Papers (5 papers)

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Research

20 pages, 4244 KiB  
Article
Edge-Optimized Lightweight YOLO for Real-Time SAR Object Detection
by Caiguang Zhang, Ruofeng Yu, Shuwen Wang, Fatong Zhang, Shaojia Ge, Shuangshuang Li and Xuezhou Zhao
Remote Sens. 2025, 17(13), 2168; https://doi.org/10.3390/rs17132168 - 24 Jun 2025
Viewed by 30
Abstract
Synthetic Aperture Radar image object detection holds significant application value in both military and civilian domains. However, existing deep learning-based methods suffer from excessive model parameters and high computational costs, making them impractical for real-time deployment on edge computing platforms. To address these [...] Read more.
Synthetic Aperture Radar image object detection holds significant application value in both military and civilian domains. However, existing deep learning-based methods suffer from excessive model parameters and high computational costs, making them impractical for real-time deployment on edge computing platforms. To address these challenges, this paper proposes a lightweight SAR object detection method optimized for edge devices. First, we design an efficient backbone network based on inverted residual blocks and the information bottleneck principle, achieving an optimal balance between feature extraction capability and computational resource consumption. Then, a Fast Feature Pyramid Network is constructed to enable efficient multi-scale feature fusion. Finally, we propose a decoupled network-in-network Head, which significantly reduces the computational overhead while maintaining detection accuracy. Experimental results demonstrate that the proposed method achieves comparable detection performance to state-of-the-art YOLO variants while drastically reducing computational complexity (4.4 GFLOP) and parameter count (1.9 M). On edge platforms (Jetson TX2 and Huawei Atlas DK 310), the model achieves real-time inference speeds of 34.2 FPS and 30.7 FPS, respectively, proving its suitability for resource-constrained, real-time SAR object detection scenarios. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Image Object Detection and Information Extraction: Methods and Applications (Second Edition))
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24 pages, 29179 KiB  
Article
SAR 3D Reconstruction Based on Multi-Prior Collaboration
by Yangyang Wang, Zhenxiao Zhou, Zhiming He, Xu Zhan, Jiapan Yu, Xingcheng Han, Xiaoling Zhang, Zhiliang Yang and Jianping An
Remote Sens. 2025, 17(12), 2105; https://doi.org/10.3390/rs17122105 - 19 Jun 2025
Viewed by 215
Abstract
Array synthetic aperture radar (SAR) three-dimensional (3D) image reconstruction enables the extraction of target distribution information in 3D space, supporting scattering characteristic analysis and structural interpretation. SAR image reconstruction remains challenging due to issues such as noise contamination and incomplete echo data. By [...] Read more.
Array synthetic aperture radar (SAR) three-dimensional (3D) image reconstruction enables the extraction of target distribution information in 3D space, supporting scattering characteristic analysis and structural interpretation. SAR image reconstruction remains challenging due to issues such as noise contamination and incomplete echo data. By introducing sparse priors such as L1 regularization functions, image quality can be improved to a certain extent and the impact of noise can be reduced. However, in scenarios involving distributed targets, the aforementioned methods often fail to maintain continuous structural features such as edges and contours, thereby limiting their reconstruction performance and adaptability. Recent studies have introduced geometric regularization functions to preserve the structural continuity of targets, yet these lack multi-prior consensus, resulting in limited reconstruction quality and robustness in complex scenarios. To address the above issues, a novel array SAR 3D reconstruction method based on multi-prior collaboration (ASAR-MPC) is proposed in this article. In this method, firstly, each optimization module in 3D reconstruction based on multi-prior is treated as an independent function module, and these modules are reformulated as parallel operations rather than sequential utilization. During the reconstruction process, the solution is constrained within the solution space of the module, ensuring that the SAR image simultaneously satisfies multiple prior conditions and achieves a coordinated balance among different priors. Then, a collaborative equilibrium framework based on Mann iteration is presented to solve the optimization problem of 3D reconstruction, which can ensure convergence to an equilibrium point and achieve the joint optimization of all modules. Finally, a series of simulation and experimental tests are described to validate the proposed method. The experimental results show that under limited echo and noise conditions, the proposed method outperforms existing methods in reconstructing complex target structures. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Image Object Detection and Information Extraction: Methods and Applications (Second Edition))
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18 pages, 2585 KiB  
Article
Incremental SAR Automatic Target Recognition with Divergence-Constrained Class-Specific Dictionary Learning
by Xiaojie Ma, Xusong Bu, Dezhao Zhang, Zhaohui Wang and Jing Li
Remote Sens. 2025, 17(12), 2090; https://doi.org/10.3390/rs17122090 - 18 Jun 2025
Viewed by 122
Abstract
Synthetic aperture radar (SAR) automatic target recognition (ATR) plays a pivotal role in SAR image interpretation. While existing approaches predominantly rely on batch learning paradigms, their practical deployment is constrained by the sequential arrival of training data and high retraining costs. To overcome [...] Read more.
Synthetic aperture radar (SAR) automatic target recognition (ATR) plays a pivotal role in SAR image interpretation. While existing approaches predominantly rely on batch learning paradigms, their practical deployment is constrained by the sequential arrival of training data and high retraining costs. To overcome this challenge, this paper introduces a divergence-constrained incremental dictionary learning framework that enables progressive model updates without full data reprocessing. Specifically, firstly, this method learns class-specific dictionaries for each target category via sub-dictionary learning, where the learning process for a specific class does not involve data from other classes. Secondly, the intra-class divergence constraint is incorporated during sub-dictionary learning to address the challenges of significant intra-class variations and minor inter-class differences in SAR targets. Thirdly, the sparse representation coefficients of the target to be classified are solved across all sub-dictionaries, followed by the computation of corresponding reconstruction errors and intra-class divergence metrics to achieve classification. Finally, when the targets of new categories are obtained, the corresponding class-specific dictionaries are calculated and added to the learned dictionary set. In this way, the incremental update of the SAR ATR system is completed. Experimental results on the MSTAR dataset indicate that our method attains >96.62% accuracy across various incremental scenarios. Compared with other state-of-the-art methods, it demonstrates better recognition performance and robustness. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Image Object Detection and Information Extraction: Methods and Applications (Second Edition))
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23 pages, 11308 KiB  
Article
TIAR-SAR: An Oriented SAR Ship Detector Combining a Task Interaction Head Architecture with Composite Angle Regression
by Yu Gu, Minding Fang and Dongliang Peng
Remote Sens. 2025, 17(12), 2049; https://doi.org/10.3390/rs17122049 - 13 Jun 2025
Viewed by 225
Abstract
Oriented ship detection in Synthetic Aperture Radar (SAR) images has broad applications in maritime surveillance and other fields. While deep learning advancements have significantly improved ship detection performance, persistent challenges remain for existing methods. These include the inherent misalignment between regression and classification [...] Read more.
Oriented ship detection in Synthetic Aperture Radar (SAR) images has broad applications in maritime surveillance and other fields. While deep learning advancements have significantly improved ship detection performance, persistent challenges remain for existing methods. These include the inherent misalignment between regression and classification tasks and the boundary discontinuity problem in oriented object detection. These issues hinder efficient and accurate ship detection in complex scenarios. To address these challenges, we propose TIAR-SAR, a novel oriented SAR ship detector featuring a task interaction head and composite angle regression. First, we propose a task interaction detection head (Tihead) capable of predicting both oriented bounding boxes (OBBs) and horizontal bounding boxes (HBBs) simultaneously. Within the Tihead, a “decompose-then-interact” structure is designed. This structure not only mitigates feature misalignment but also promotes feature interaction between regression and classification tasks, thereby enhancing prediction consistency. Second, we propose a joint angle refinement mechanism (JARM). The JARM addresses the non-differentiability problem of the traditional rotated Intersection over Union (IoU) loss through the design of a composite angle regression loss (CARL) function, which strategically combines direct and indirect angle regression methods. A boundary angle correction mechanism (BACM) is then designed to enhance angle estimation accuracy. During inference, BACM dynamically replaces an object’s OBB prediction with its corresponding HBB if the OBB exhibits excessive angle deviation when the angle of the object is near the predefined boundary. Finally, the performance and applicability of the proposed methods are evaluated through extensive experiments on multiple public datasets, including SRSDD, HRSID, and DOTAv1. Experimental results derived from the use of the SRSDD dataset demonstrate that the mAP50 of the proposed method reaches 63.91%, an improvement of 4.17% compared with baseline methods. The detector achieves 17.42 FPS on 1024 × 1024 images using an RTX 2080 Ti GPU, with a model size of only 21.92 MB. Comparative experiments with other state-of-the-art methods on the HRSID dataset demonstrate the proposed method’s superior detection performance in complex nearshore scenarios. Furthermore, when further tested on the DOTAv1 dataset, the mAP50 can reach 79.1%. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Image Object Detection and Information Extraction: Methods and Applications (Second Edition))
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36 pages, 6489 KiB  
Article
Improving SAR Ship Detection Accuracy by Optimizing Polarization Modes: A Study of Generalized Compact Polarimetry (GCP) Performance
by Guo Song, Yunkai Deng, Heng Zhang, Xiuqing Liu and Sheng Chang
Remote Sens. 2025, 17(11), 1951; https://doi.org/10.3390/rs17111951 - 5 Jun 2025
Viewed by 459
Abstract
The debate surrounding the optimal polarimetric modes—compact polarimetry (CP) versus dual polarization (DP)—for PolSAR ship detection persists. This study pioneers a systematic investigation into Generalized Compact Polarimetry (GCP) for this application. By synthesizing and evaluating 143 distinct GCP configurations from fully polarimetric data, [...] Read more.
The debate surrounding the optimal polarimetric modes—compact polarimetry (CP) versus dual polarization (DP)—for PolSAR ship detection persists. This study pioneers a systematic investigation into Generalized Compact Polarimetry (GCP) for this application. By synthesizing and evaluating 143 distinct GCP configurations from fully polarimetric data, this study presents the first comprehensive comparison of their ship detection performance against conventional modes using Target-to-Clutter Ratio (TCR) and deep learning-based accuracy (AP50). Experiments on the FPSD dataset reveal that an optimized GCP mode (e.g., ellipse/orientation: [−10, −5]) consistently outperforms traditional CP and DP modes, yielding TCR gains of 0.2–2.7 dB. This translates to AP50 improvements of 0.5–4.7% (Faster R-CNN) and 0.1–5.5% (RetinaNet) over five common baseline modes. Crucially, this enhancement arises from optimizing the interaction between the polarization mode and target/clutter scattering characteristics rather than algorithmic improvements, supporting the proposed “optimization from the information source” strategy. These findings offer significant implications for future PolSAR system design and operational mode selection. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Image Object Detection and Information Extraction: Methods and Applications (Second Edition))
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