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SAR Image Object Detection and Information Extraction: Methods and Applications

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

Deadline for manuscript submissions: closed (28 April 2025) | Viewed by 7694

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Dr. Kefeng Ji

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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

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Guest Editor

School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
Interests: SAR image object detection; computer vision; pattern recognition; machine learning
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Dr. Xiangguang Leng

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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
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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
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Dr. Zhongzhen Sun

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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 are of great significance in both military and 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 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

We look forward to receiving your contributions.

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

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

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Research

21 pages, 6270 KiB

Open AccessArticle

Cross-Level Adaptive Feature Aggregation Network for Arbitrary-Oriented SAR Ship Detection

by Lu Qian, Junyi Hu, Haohao Ren, Jie Lin, Xu Luo, Lin Zou and Yun Zhou

Remote Sens. 2025, 17(10), 1770; https://doi.org/10.3390/rs17101770 - 19 May 2025

Viewed by 149

Abstract

The rapid progress of deep learning has significantly enhanced the development of ship detection using synthetic aperture radar (SAR). However, the diversity of ship sizes, arbitrary orientations, densely arranged ships, etc., have been hindering the improvement of SAR ship detection accuracy. In response to these challenges, this study introduces a new detection approach called a cross-level adaptive feature aggregation network (CLAFANet) to achieve arbitrary-oriented multi-scale SAR ship detection. Specifically, we first construct a hierarchical backbone network based on a residual architecture to extract multi-scale features of ship objects from large-scale SAR imagery. Considering the multi-scale nature of ship objects, we then resort to the idea of self-attention to develop a cross-level adaptive feature aggregation (CLAFA) mechanism, which can not only alleviate the semantic gap between cross-level features but also improve the feature representation capabilities of multi-scale ships. To better adapt to the arbitrary orientation of ship objects in real application scenarios, we put forward a frequency-selective phase-shifting coder (FSPSC) module for arbitrary-oriented SAR ship detection tasks, which is dedicated to mapping the rotation angle of the object bounding box to different phases and exploits frequency-selective phase-shifting to solve the periodic ambiguity problem of the rotated bounding box. Qualitative and quantitative experiments conducted on two public datasets demonstrate that the proposed CLAFANet achieves competitive performance compared to some state-of-the-art methods in arbitrary-oriented SAR ship detection. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

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18 pages, 7236 KiB

Open AccessArticle

LPFFNet: Lightweight Prior Feature Fusion Network for SAR Ship Detection

by Xiaozhen Ren, Peiyuan Zhou, Xiaqiong Fan, Chengguo Feng and Peng Li

Remote Sens. 2025, 17(10), 1698; https://doi.org/10.3390/rs17101698 - 12 May 2025

Viewed by 184

Abstract

SAR ship detection is of great significance in marine safety, fisheries management, and maritime traffic. At present, many deep learning-based ship detection methods have improved the detection accuracy but also increased the complexity and computational cost. To address the issue, a lightweight prior feature fusion network (LPFFNet) is proposed to better improve the performance of SAR ship detection. A perception lightweight backbone network (PLBNet) is designed to reduce model complexity, and a multi-channel feature enhancement module (MFEM) is introduced to enhance the SAR ship localization capability. Moreover, a channel prior feature fusion network (CPFFNet) is designed to enhance the perception ability of ships of different sizes. Meanwhile, the residual channel focused attention module (RCFA) and the multi-kernel adaptive pooling local attention network (MKAP-LAN) are integrated to improve feature extraction capability. In addition, the enhanced ghost convolution (EGConv) is used to generate more reliable gradient information. And finally, the detection performance is improved by focusing on difficult samples through a smooth weighted focus loss function (SWF Loss). The experimental results have verified the effectiveness of the proposed model. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

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19 pages, 12426 KiB

Open AccessArticle

Oriented SAR Ship Detection Based on Edge Deformable Convolution and Point Set Representation

by Tianyue Guan, Sheng Chang, Yunkai Deng, Fengli Xue, Chunle Wang and Xiaoxue Jia

Remote Sens. 2025, 17(9), 1612; https://doi.org/10.3390/rs17091612 - 1 May 2025

Viewed by 323

Abstract

Ship detection in synthetic aperture radar (SAR) images holds significant importance for both military and civilian applications, including maritime traffic supervision, marine search and rescue operations, and emergency response initiatives. Although extensive research has been conducted in this field, the interference of speckle noise in SAR images and the potential discontinuity of target contours continue to pose challenges for the accurate detection of multi-directional ships in complex scenes. To address these issues, we propose a novel ship detection method for SAR images that leverages edge deformable convolution combined with point set representation. By integrating edge deformable convolution with backbone networks, we learn the correlations between discontinuous target blocks in SAR images. This process effectively suppresses speckle noise while capturing the overall offset characteristics of targets. On this basis, a multi-directional ship detection module utilizing radial basis function (RBF) point set representation is developed. By constructing a point set transformation function, we establish efficient geometric alignment between the point set and the predicted rotated box, and we impose constraints on the penalty term associated with point set transformation to ensure accurate mapping between point set features and directed prediction boxes. This methodology enables the precise detection of multi-directional ship targets even in dense scenes. The experimental results derived from two publicly available datasets, RSDD-SAR and SSDD, demonstrate that our proposed method achieves state-of-the-art performance when benchmarked against other advanced detection models. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

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25 pages, 9418 KiB

Open AccessArticle

Angle-Controllable SAR Image Generation for Target Recognition with Few Samples

by Xilin Wang, Bingwei Hui, Wei Wang, Pengcheng Guo, Lei Ding and Huangxing Lin

Remote Sens. 2025, 17(7), 1206; https://doi.org/10.3390/rs17071206 - 28 Mar 2025

Viewed by 268

Abstract

The availability of high-quality and ample synthetic aperture radar (SAR) image datasets is crucial for understanding and recognizing target characteristics. However, in practical applications, the limited availability of SAR target images significantly impedes the advancement of SAR interpretation methodologies. In this study, we introduce a Generative Adversarial Network (GAN)-based approach designed to manipulate the target azimuth angle with few samples, thereby generating high-quality target images with adjustable angle ranges. The proposed method consists of three modules: a generative fusion local module conditioned on image features, a controllable angle generation module based on sparse representation, and an angle discrimination module based on scattering point extraction. Consequently, the generative modules fuse semantically aligned features from different images to produce diverse SAR samples, whereas the angle synthesis module constructs target images within a specified angle range. The discriminative module comprises a similarity discriminator to distinguish between authentic and synthetic images to ensure the image quality, and an angle discriminator to verify that generated images conform to the specified range of the azimuth angle. Combining these modules, the proposed methodology is capable of generating azimuth angle-controllable target images using only a limited number of support samples. The effectiveness of the proposed method is not only verified through various quality metrics, but also examined through the enhanced distinguishability of target recognition methods. In our experiments, we achieved SAR image generation within a given angle range on two datasets. In terms of generated image quality, our method has significant advantages over other methods in metrics such as FID and SSIM. Specifically, the FID was reduced by up to 0.37, and the SSIM was increased by up to 0.46. In the target recognition experiments, after augmenting the data, the accuracy improved by 6.16% and 3.29% under two different pitch angles, respectively. This demonstrates that our method has great advantages in the SAR image generation task, and the research content is of great value. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

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18 pages, 5757 KiB

Open AccessArticle

Detection-Oriented Evaluation of SAR Dexterous Barrage Jamming Effectiveness

by Hai Zhu, Sinong Quan, Shiqi Xing, Haoyu Zhang and Yun Ren

Remote Sens. 2025, 17(6), 1101; https://doi.org/10.3390/rs17061101 - 20 Mar 2025

Viewed by 314

Abstract

The assessment of the jamming effect of Synthetic Aperture Radar (SAR) is the primary means to measure the reliability of the jamming, which can provide important guidance for the use of jamming strategies and patterns. This paper proposes a detection-oriented evaluation of the effect of SAR dexterous barrage jamming. Starting from the detection, it divides the evaluation process into two stages: (1) for the case in which the target can be detected under the jamming scenario, two feature parameters, namely, the target exposion area and target relative magnitude, are extracted; (2) for the case in which the target cannot be detected under the jamming scenario, another three feature parameters, namely, jamming relative magnitude, average edge brightness, and local information entropy, are extracted. On this basis, two hierarchical evaluation candidates, the target exposure degree and jamming concealment degree, respectively, are designed, and a comprehensive evaluation index of the dexterous suppression degree is finally proposed. Jamming experiments are carried out from real and simulated SAR data with different scenarios, and the results demonstrate that the proposed method effectively measures the barrage jamming effects of different jamming-to-signal ratios and patterns. More importantly, it quantifies the relationship between suppression degree and detection rate, wherein the detection rate decreases by about 35% to 45% for every 0.1 increase in the suppression degree. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

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25 pages, 20488 KiB

Open AccessArticle

SAR Small Ship Detection Based on Enhanced YOLO Network

by Tianyue Guan, Sheng Chang, Chunle Wang and Xiaoxue Jia

Remote Sens. 2025, 17(5), 839; https://doi.org/10.3390/rs17050839 - 27 Feb 2025

Cited by 3 | Viewed by 1137

Abstract

Ships are important targets for marine surveillance in both military and civilian domains. Since the rise of deep learning, ship detection in synthetic aperture radar (SAR) images has achieved significant progress. However, the variability in ship size and resolution, especially the widespread presence of numerous small-sized ships, continues to pose challenges for effective ship detection in SAR images. To address the challenges posed by small ship targets, we propose an enhanced YOLO network to improve the detection accuracy of small targets. Firstly, we propose a Shuffle Re-parameterization (SR) module as a replacement for the C2f module in the original YOLOv8 network. The SR module employs re-parameterized convolution along with channel shuffle operations to improve feature extraction capabilities. Secondly, we employ the space-to-depth (SPD) module to perform down-sampling operations within the backbone network, thereby reducing the information loss associated with pooling operations. Thirdly, we incorporate a Hybrid Attention (HA) module into the neck network to enhance the feature representation of small ship targets while mitigating the interference caused by surrounding sea clutter and speckle noise. Finally, we add the shape-NWD loss to the regression loss, which emphasizes the shape and scale of the bounding box and mitigates the sensitivity of Intersection over Union (IoU) to positional deviations in small ship targets. Extensive experiments were carried out on three publicly available datasets—namely, LS-SSDD, HRSID, and iVision-MRSSD—to demonstrate the effectiveness and reliability of the proposed method. In the small ship dataset LS-SSDD, the proposed method exhibits a notable improvement in average precision at an IoU threshold of 0.5 (AP50), surpassing the baseline network by over 4%, and achieving an AP50 of 77.2%. In the HRSID and iVision-MRSSD datasets, AP50 reaches 91% and 95%, respectively. Additionally, the average precision for small targets (AP) exhibits an increase of approximately 2% across both datasets. Furthermore, the proposed method demonstrates outstanding performance in comparison experiments across all three datasets, outperforming existing state-of-the-art target detection methods. The experimental results offer compelling evidence supporting the superior performance and practical applicability of the proposed method in SAR small ship detection. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

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28 pages, 11323 KiB

Open AccessArticle

Polarimetric SAR Ship Detection Using Context Aggregation Network Enhanced by Local and Edge Component Characteristics

by Canbin Hu, Hongyun Chen, Xiaokun Sun and Fei Ma

Remote Sens. 2025, 17(4), 568; https://doi.org/10.3390/rs17040568 - 7 Feb 2025

Cited by 1 | Viewed by 671

Abstract

Polarimetric decomposition methods are widely used in polarimetric Synthetic Aperture Radar (SAR) data processing for extracting scattering characteristics of targets. However, polarization SAR methods for ship detection still face challenges. The traditional constant false alarm rate (CFAR) detectors face sea clutter modeling and parameter estimation problems in ship detection, which is difficult to adapt to the complex background. In addition, neural network-based detection methods mostly rely on single polarimetric-channel scattering information and fail to fully explore the polarization properties and physical scattering laws of ships. To address these issues, this study constructed two novel characteristics: a helix-scattering enhanced (HSE) local component and a multi-scattering intensity difference (MSID) edge component, which are specifically designed to describe ship scattering characteristics. Based on the characteristic differences of different scattering components in ships, this paper designs a context aggregation network enhanced by local and edge component characteristics to fully utilize the scattering information of polarized SAR data. With the powerful feature extraction capability of a convolutional neural network, the proposed method can significantly enhance the distinction between ships and the sea. Further analysis shows that HSE is able to capture structural information about the target, MSID can increase ship–sea separation capability, and an HV channel retains more detailed information. Compared with other decomposition models, the proposed characteristic combination model performs well in complex backgrounds and can distinguish ship from sea more effectively. The experimental results show that the proposed method achieves a detection precision of 93.6% and a recall rate of 91.5% on a fully polarized SAR dataset, which are better than other popular network algorithms, verifying the reasonableness and superiority of the method. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

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27 pages, 69359 KiB

Open AccessArticle

Few-Shot Object Detection for SAR Images via Context-Aware and Robust Gaussian Flow Representation

by Po Zhao, Jie Chen, Huiyao Wan, Yice Cao, Shuai Wang, Yan Zhang, Yingsong Li, Zhixiang Huang and Bocai Wu

Remote Sens. 2025, 17(3), 391; https://doi.org/10.3390/rs17030391 - 23 Jan 2025

Viewed by 1053

Abstract

In recent decades, few-shot object detection in SAR imagery has gained prominence as a major research focus. The unique imaging mechanism of SAR causes the model to suffer from foreground–background imbalance and inaccurate extraction of class prototypes for novel class instances. Therefore, we propose an innovative few-shot object detection algorithm for SAR images via context-aware and robust Gaussian flow representation. First, we design the Context-Aware Enhancement module to address the foreground–context imbalance problem by refining representative support features into fine-grained prototypes, which are deeply fused with query features based on the prototype matching paradigm. Second, we devise the Manifold Class Distribution Estimation module to address the difficulty of class distribution estimation and the fluctuation of class centers of the sparse novel class. Furthermore, we formulate the Category-Balanced Difference Aggregation module to model the relationship between the base class and the novel class, addressing the sensitivity of the model to the variance of the novel class instances. Finally, we design the Cosine Decoupling Module so that the aggregated features are executed only on the classification branch without affecting the precise localization of the target. Experiments based on SAR-AIRcraft-1.0 and the self-constructed MSAR-AIR dataset indicate that the fine-grained detection and identification performance of the novel class of airplanes can reach 32.90% and 55.26%, respectively, in the 10-shot and 50-shot cases. In addition, our method enables cross-domain detection for different scenarios and sample types and exhibits excellent generalization performance in data-sparse scenarios. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

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19 pages, 5807 KiB

Open AccessArticle

BurgsVO: Burgs-Associated Vertex Offset Encoding Scheme for Detecting Rotated Ships in SAR Images

by Mingjin Zhang, Yaofei Li, Jie Guo, Yunsong Li and Xinbo Gao

Remote Sens. 2025, 17(3), 388; https://doi.org/10.3390/rs17030388 - 23 Jan 2025

Viewed by 584

Abstract

Synthetic Aperture Radar (SAR) is a crucial remote sensing technology with significant advantages. Ship detection in SAR imagery has garnered significant attention. However, existing ship detection methods often overlook feature extraction, and the unique imaging mechanisms of SAR images hinder the direct application of conventional natural image feature extraction techniques. Moreover, oriented bounding box-based detection methods often prioritize accuracy excessively, leading to increased parameters and computational costs, which in turn elevate computational load and model complexity. To address these issues, we propose a novel two-stage detector, Burgs-rooted vertex offset encoding scheme (BurgsVO), for detecting rotated ships in SAR images. BurgsVO consists of two key modules: the Burgs equation heuristics module, which facilitates feature extraction, and the average diagonal vertex offset (ADVO) encoding scheme, which significantly reduces computational costs. Specifically, the Burgs equation module integrates temporal information with spatial data for effective feature aggregation, establishing a strong foundation for subsequent object detection. The ADVO encoding scheme reduces parameters through anchor transformation, leveraging geometric similarities between quadrilaterals and triangles to further reduce computational costs. Experimental results on the RSSDD and RSDD benchmarks demonstrate that the proposed BurgsVO outperforms the state-of-the-art detectors in both accuracy and efficiency. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

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26 pages, 5245 KiB

Open AccessArticle

An Imaging Method for Marine Targets in Corner Reflector Jamming Scenario Based on Time–Frequency Analysis and Modified Clean Technique

by Changhong Chen, Wenkang Liu, Yuexin Gao, Lei Cui, Quan Chen, Jixiang Fu and Mengdao Xing

Remote Sens. 2025, 17(2), 310; https://doi.org/10.3390/rs17020310 - 16 Jan 2025

Viewed by 665

Abstract

In the corner reflector jamming scenario, the ship target and the corner reflector array have different degrees of defocusing in the synthetic aperture radar (SAR) image due to their complex motions, which is unfavorable to the subsequent target recognition. In this manuscript, we propose an imaging method for marine targets based on time–frequency analysis with the modified Clean technique. Firstly, the motion models of the ship target and the corner reflector array are established, and the characteristics of their Doppler parameter distribution are analyzed. Then, the Chirp Rate–Quadratic Chirp Rate Distribution (CR-QCRD) algorithm is utilized to estimate the Doppler parameters. To address the challenges posed by the aggregated scattering points of the ship target and the overlapping Doppler histories of the corner reflector array, the Clean technique is modified by short-time Fourier transform (STFT) filtering and amplitude–phase distortion correction using fractional Fourier transform (FrFT) filtering. This modification aims to improve the accuracy and efficiency of extracting scattering point components. Thirdly, in response to the poor universality of the traditional Clean iterative termination condition, the kurtosis of the residual signal spectrum amplitude is adopted as the new iterative termination condition. Compared with the existing imaging methods, the proposed method can adapt to the different Doppler distribution characteristics of the ship target and the corner reflector array, thus realizing better robustness in obtaining a well-focused target image. Finally, simulation experiments verify the effectiveness of the algorithm. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

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23 pages, 10942 KiB

Open AccessArticle

MambaShadowDet: A High-Speed and High-Accuracy Moving Target Shadow Detection Network for Video SAR

by Xiaowo Xu, Tianwen Zhang, Xiaoling Zhang, Wensi Zhang, Xiao Ke and Tianjiao Zeng

Remote Sens. 2025, 17(2), 214; https://doi.org/10.3390/rs17020214 - 9 Jan 2025

Cited by 1 | Viewed by 1148

Abstract

Existing convolution neural network (CNN)-based video synthetic aperture radar (SAR) moving target shadow detectors are difficult to model long-range dependencies, while transformer-based ones often suffer from greater complexity. To handle these issues, this paper proposes MambaShadowDet, a novel lightweight deep learning (DL) detector based on a state space model (SSM), dedicated to high-speed and high-accuracy moving target shadow detection in video SAR images. By introducing SSM with the linear complexity into YOLOv8, MambaShadowDet effectively captures the global feature dependencies while relieving computational load. Specifically, it designs Mamba-Backbone, combining SSM and CNN to effectively extract both global contextual and local spatial information, as well as a slim path aggregation feature pyramid network (Slim-PAFPN) to enhance multi-level feature extraction and further reduce complexity. Abundant experiments on the Sandia National Laboratories (SNL) video SAR data show that MambaShadowDet achieves superior moving target shadow detection performance with a detection accuracy of 80.32% F1 score and an inference speed of 44.44 frames per second (FPS), outperforming existing models in both accuracy and speed. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

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