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Target Recognition and Detection Based on High Resolution Radar Images...
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Target Recognition and Detection Based on High Resolution Radar Images (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: 30 September 2026 | Viewed by 3511

Editors

Prof. Dr. Fan Zhang

E-Mail Website
Guest Editor
1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China 2. Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing 100029, China
Interests: image processing; artificial intelligence; remote sensing; high performance computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Huiyu Zhou

E-Mail Website
Guest Editor
School of Computing and Mathematic Sciences, University of Leicester, Leicester LE1 7RH, UK
Interests: machine learning; computer vision; intelligent systems; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fei Gao

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Interests: radar
Special Issues, Collections and Topics in MDPI journals
Dr. Lixiang Ma

E-Mail Website
Guest Editor
Institute of Remote Sensing Satellite , China Academy of Space Techjnology, Beijing 100094, China
Interests: interferometric synthetic aperture radar (InSAR); satellite remote sensing; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Fei Ma

E-Mail Website
Guest Editor
College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
Interests: synthetic aperture radar (SAR); target detection; target recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the great support for and interest in the previous Special Issue, we can now announce a second edition of “Target Recognition and Detection Based on High-Resolution Radar Images." We would like to thank all the authors and co-authors who contributed to the successful first edition of this Special Issue and we look forward to receiving more expert and innovative contributions.

The capacity to successfully detect and identify objects in radar imaging has important implications for environmental protection and other applications. With the development of modern radar systems that are capable of producing high-quality pictures, novel algorithms and approaches that enhance the accuracy and reliability of target identification and localization have been developed. We are pleased to invite you to submit your papers to this Special Issue of Remote Sensing, entitled "Target Recognition and Detection Based on High-Resolution Radar Images". This Special Issue focuses on novel research and technologies that aim to enhance target recognition and detection abilities using high-resolution radar pictures, exploring the application of these techniques in various domains. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Radar image dataset;
  • Synthetic aperture radar (SAR) image processing;
  • Novel feature extraction methods;
  • SAR image interpretation;
  • Automatic target recognition;
  • Target detection;
  • Deep learning-based approaches (big models, etc.);
  • Other radar image applications.

Prof. Dr. Fan Zhang
Prof. Dr. Huiyu Zhou
Prof. Dr. Fei Gao
Dr. Lixiang Ma
Dr. Fei Ma
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 250 words) can be sent to the Editorial Office for assessment.

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

  • high-resolution radar images
  • synthetic aperture radar (SAR)
  • target detection
  • target recognition
  • deep learning
  • machine learning
  • image processing

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Related Special Issue

Published Papers (5 papers)

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Research

29 pages, 6688 KB  
Article
CGMSN: CFAR-Guided Mode-Selective Network for SAR Target Detection
by Lingjuan Yu, Xinya Xiong, Xiaochun Xie, Miaomiao Liang, Xiangchun Yu, Xuan Jiao and Wen Hong
Remote Sens. 2026, 18(12), 2040; https://doi.org/10.3390/rs18122040 - 18 Jun 2026
Abstract
Improving detection performance across diverse synthetic aperture radar (SAR) scenes remains challenging because different datasets exhibit different levels of target–background separability. To address this issue, we propose a constant false alarm rate (CFAR)-guided mode-selective network (CGMSN), which selects an appropriate feature-fusion mode according [...] Read more.
Improving detection performance across diverse synthetic aperture radar (SAR) scenes remains challenging because different datasets exhibit different levels of target–background separability. To address this issue, we propose a constant false alarm rate (CFAR)-guided mode-selective network (CGMSN), which selects an appropriate feature-fusion mode according to the CFAR target–background separation margin. Specifically, CFAR is used as an interpretable statistical tool to construct an anomaly response map. The separation margin is then calculated by comparing the average CFAR anomaly responses of annotated target regions and their surrounding contextual backgrounds. Based on this indicator, a You Only Look Once version 8 (YOLOv8)-based mode-selective detector is constructed with three key components. First, a lightweight representation-enhanced backbone that integrates ResNet18 and a dilated convolutional spatial pyramid (DCSP) module is adopted to improve contextual representation while maintaining moderate model complexity. Second, a mode-selective neck (MSN) is designed with three predefined fusion modes, where the appropriate fusion depth is selected according to the CFAR-guided target–background separation margin of each dataset. Third, a complete intersection over the union modulated head (CMH) is developed to enhance classification-regression alignment and suppress clutter-induced responses. Experiments on SAR-Aircraft-1.0, High-Resolution SAR Images Dataset (HRSID), and SAR Ship Detection Dataset (SSDD) indicate that datasets with smaller CFAR target–background separation margins benefit from deeper fusion, while datasets with larger separation margins can adopt shallower fusion. Moreover, the proposed CGMSN achieves superior performance over representative detectors, demonstrating its effectiveness on the evaluated SAR datasets with diverse scene characteristics. Full article
(This article belongs to the Special Issue Target Recognition and Detection Based on High Resolution Radar Images (Second Edition))
24 pages, 16915 KB  
Article
An Image Stabilization Method for Airborne Video SAR Based on a Joint Singer-Random Walk Model
by Yanping Wang, Shuo Wang, Zhirui Wang and Guanyong Wang
Remote Sens. 2026, 18(10), 1500; https://doi.org/10.3390/rs18101500 - 10 May 2026
Viewed by 309
Abstract
Video synthetic aperture radar (ViSAR) provides continuous multiframe images while maintaining high resolution and has become an important tool for complex scene surveillance and moving target tracking. ViSAR imaging is susceptible to interframe drift caused by motion errors, which severely degrades video stability. [...] Read more.
Video synthetic aperture radar (ViSAR) provides continuous multiframe images while maintaining high resolution and has become an important tool for complex scene surveillance and moving target tracking. ViSAR imaging is susceptible to interframe drift caused by motion errors, which severely degrades video stability. When registering long time series of real airborne video SAR images, conventional image registration based on Normalized Cross-Correlation (NCC) is affected by several factors, including platform residual motion errors, approximations in the imaging geometry, interpolation resampling, and SAR speckle noise. As a result, noticeable interframe jitter persists in the registered sequence, and the stabilization accuracy is insufficient to meet high-precision image stabilization requirements. To address these issues, this paper proposes an image stabilization method for airborne video SAR based on a joint Singer-random walk model. Firstly, with the first frame selected as the reference, subpixel drift measurements in the azimuth and range directions are extracted from continuous frames via NCC-based registration. Subsequently, the true drift is modeled as a two-dimensional Singer process and the systematic bias as a random walk process, yielding a joint state space model that comprises displacement, velocity, acceleration, and bias components. On this basis, a Kalman filter and a Rauch–Tung–Striebel (RTS) fixed-interval smoother are applied to perform temporal filtering and trajectory smoothing on the drift measurements, thereby producing smooth two-dimensional drift estimates that closely approximate the actual drift trajectory. Finally, the smoothed drift trajectory is used to perform frame-by-frame subpixel drift correction on the original image sequence, achieving high-precision interframe stabilization of the ViSAR imagery. The results of real data processing demonstrate that the proposed method can effectively improve the consistency and scene stability of ViSAR multi-frame imaging. Full article
(This article belongs to the Special Issue Target Recognition and Detection Based on High Resolution Radar Images (Second Edition))
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24 pages, 15070 KB  
Article
HGXES: Lightweight Network for Ship Detection in Specific Marine Environments
by Yang Tian, Fei Gao, Rongfeng Huang and Yongliang Wu
Remote Sens. 2026, 18(9), 1276; https://doi.org/10.3390/rs18091276 - 23 Apr 2026
Viewed by 432
Abstract
Synthetic Aperture Radar (SAR) ship target detection is crucial for marine monitoring, offering vital support for maritime security, navigation safety, and environmental surveillance. However, deploying advanced deep learning models on resource-constrained edge devices like UAVs and spaceborne platforms is challenging due to the [...] Read more.
Synthetic Aperture Radar (SAR) ship target detection is crucial for marine monitoring, offering vital support for maritime security, navigation safety, and environmental surveillance. However, deploying advanced deep learning models on resource-constrained edge devices like UAVs and spaceborne platforms is challenging due to the high computational complexity and large parameter counts, hindering real-time performance. To address this, we propose the HGXES model, a lightweight SAR ship detection network. This model integrates efficient structural design, feature enhancement mechanisms, and an attention mechanism to reduce computational costs while preserving feature extraction capabilities. It employs factorized convolutions, a cross-level feature reuse module, and an attention mechanism to dynamically adjust feature weights, enhancing sensitivity to ship targets. A lightweight detection head ensures rapid and accurate target classification and localization. Experiments on benchmark SAR datasets show that based on the lightweight HGNetV2 backbone, our incremental designs (Xfeat, ELA, LWDetect) further compress the model and achieve a 70% reduction in parameters compared with traditional models, with a model size of just 1.9 MB, 2.3 M parameters, and 3.9 G FLOPs, achieving 49.7 fps detection speed. Comparative analyses reveal the superiority of the ELA attention mechanism and ShapeIoU loss function in enhancing performance. Thus, the HGXES model successfully achieves lightweight SAR ship detection, supporting real-time marine monitoring on resource-limited platforms with high accuracy and reduced computational costs. Full article
(This article belongs to the Special Issue Target Recognition and Detection Based on High Resolution Radar Images (Second Edition))
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21 pages, 3073 KB  
Article
SARDet-MIM: Enhancing SAR Target Detection via a Structural and Scattering Masked Autoencoder
by Peiling Zhou, Ben Niu, Lijia Huang, Qiantong Wang, Yongchao Zhao, Guangyao Zhou and Yuxin Hu
Remote Sens. 2026, 18(4), 580; https://doi.org/10.3390/rs18040580 - 13 Feb 2026
Viewed by 683
Abstract
The performance of deep learning approaches for Synthetic Aperture Radar (SAR) target detection is often limited by the scarcity of annotated data. While Self-Supervised Learning (SSL) has emerged as a powerful paradigm to mitigate data dependence, its potential in SAR target detection remains [...] Read more.
The performance of deep learning approaches for Synthetic Aperture Radar (SAR) target detection is often limited by the scarcity of annotated data. While Self-Supervised Learning (SSL) has emerged as a powerful paradigm to mitigate data dependence, its potential in SAR target detection remains largely underexplored. In this study, we propose SARDet-MIM, a comprehensive framework based on Masked Image Modeling (MIM), to enhance SAR target detection. The approach consists of two stages. In the self-supervised pre-training stage, we propose an innovative Structural and Scattering Masked Autoencoder (SSMAE) method for SAR imagery. Unlike conventional MIM methods, which typically reconstruct raw pixels, SSMAE employs a physics-aware reconstruction target comprising multi-scale gradient and SAR-Harris features. This strategy explicitly guides the network to capture discriminative structural contexts and intrinsic scattering features that benefit SAR target detection. For downstream detection, we construct a Maximally Pre-trained Detector (MPD), which integrally transfers the pre-trained ViT encoder–decoder architecture to the detection network to fully exploit pre-trained representations. Extensive experiments on three SAR target detection datasets demonstrate that SARDet-MIM consistently outperforms competing methods. Full article
(This article belongs to the Special Issue Target Recognition and Detection Based on High Resolution Radar Images (Second Edition))
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23 pages, 3266 KB  
Article
A 3D Reconstruction Technique for UAV SAR Under Horizontal-Cross Configurations
by Junhao He, Dong Feng, Chongyi Fan, Beizhen Bi, Fengzhuo Huang, Shuang Yue, Zhuo Xu and Xiaotao Huang
Remote Sens. 2025, 17(21), 3604; https://doi.org/10.3390/rs17213604 - 31 Oct 2025
Viewed by 1449
Abstract
Synthetic Aperture Radar (SAR) three-dimensional (3D) imaging has considerable potential in disaster monitoring and topographic mapping. Conventional 3D SAR imaging techniques for unmanned aerial vehicle (UAV) formations require rigorously regulated vertical or linear flight trajectories to maintain signal coherence. In practice, however, restricted [...] Read more.
Synthetic Aperture Radar (SAR) three-dimensional (3D) imaging has considerable potential in disaster monitoring and topographic mapping. Conventional 3D SAR imaging techniques for unmanned aerial vehicle (UAV) formations require rigorously regulated vertical or linear flight trajectories to maintain signal coherence. In practice, however, restricted collaboration precision among UAVs frequently prevents adherence to these trajectories, resulting in blurred scattering characteristics and degraded 3D localization accuracy. To address this, a 3D reconstruction technique based on horizontal-cross configurations is proposed, which establishes a new theoretical framework. This approach reduces stringent flight restrictions by transforming the requirement for vertical baselines into geometric flexibility in the horizontal plane. For dual-UAV subsystems, a geometric inversion algorithm is developed for initial scattering center localization. For multi-UAV systems, a multi-aspect fusion algorithm is proposed; it extends the dual-UAV inversion method and incorporates basis transformation theory to achieve coherent integration of multi-platform radar observations. Numerical simulations demonstrate an 80% reduction in implementation costs compared to tomographic SAR (TomoSAR), along with a 1.7-fold improvement in elevation resolution over conventional beamforming (CBF), confirming the framework’s effectiveness. This work presents a systematic horizontal-cross framework for SAR 3D reconstruction, offering a practical solution for UAV-based imaging in complex environments. Full article
(This article belongs to the Special Issue Target Recognition and Detection Based on High Resolution Radar Images (Second Edition))
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