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Remote Sensing
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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

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 June 2025 | Viewed by 5202

Special Issue 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; artificial intelligence; computer vision; human-computer interface
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
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
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 Issue Information

Dear Colleagues,

The capacity to successfully detect and identify objects in radar imaging has important implications for military surveillance, environmental protection, and other applications. With the development of modern radar systems 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, and explores 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 et al.)
  • other radar image applications.

Prof. Dr. Fan Zhang
Prof. Dr. Huiyu (Joe) 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 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

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

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Published Papers (5 papers)

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Research

27 pages, 5294 KiB  
Article
Fusion Unbiased Pseudo-Linear Kalman Filter-Based Bearings-Only Target Tracking
by Zhihao Cai, Shiqi Xing, Weize Meng, Junpeng Wang, Xinyuan Su and Sinong Quan
Remote Sens. 2024, 16(23), 4536; https://doi.org/10.3390/rs16234536 - 3 Dec 2024
Viewed by 641
Abstract
In the realm of bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) has garnered significant interest, due to its low computational demands and robust stability. However, the interrelation between the measurement matrix and noise introduces bias into the PLKF’s target state estimation. To [...] Read more.
In the realm of bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) has garnered significant interest, due to its low computational demands and robust stability. However, the interrelation between the measurement matrix and noise introduces bias into the PLKF’s target state estimation. To address this issue, we introduce a fusion unbiased PLKF (FUBKF) algorithm. This algorithm initiates with a global pseudo-linear treatment of the measurement equation, subsequently isolating the noise within the measurement matrix. By employing the unscented Kalman filter (UKF), the algorithm achieves precise estimation of the measurement matrix, thereby mitigating the estimation error stemming from the correlation between the measurement matrix and noise. Simulation outcomes demonstrate that the proposed algorithm substantially enhances tracking accuracy and sustains high stability in both 2D and 3D bearings-only target tracking scenarios, encompassing both non-maneuvering and maneuvering conditions. Full article
(This article belongs to the Special Issue Target Recognition and Detection Based on High Resolution Radar Images)
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25 pages, 23009 KiB  
Article
Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition
by Fei Gao, Xin Luo, Rongling Lang, Jun Wang, Jinping Sun and Amir Hussain
Remote Sens. 2024, 16(17), 3277; https://doi.org/10.3390/rs16173277 - 3 Sep 2024
Cited by 1 | Viewed by 762
Abstract
Current synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms primarily operate under the closed-set assumption, implying that all target classes have been previously learned during the training phase. However, in open scenarios, they may encounter target classes absent from the training set, [...] Read more.
Current synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms primarily operate under the closed-set assumption, implying that all target classes have been previously learned during the training phase. However, in open scenarios, they may encounter target classes absent from the training set, thereby necessitating an open set recognition (OSR) challenge for SAR-ATR. The crux of OSR lies in establishing distinct decision boundaries between known and unknown classes to mitigate confusion among different classes. To address this issue, we introduce a novel framework termed reinforced class separability for SAR target open set recognition (RCS-OSR), which focuses on optimizing prototype distribution and enhancing the discriminability of features. First, to capture discriminative features, a cross-modal causal features enhancement module (CMCFE) is proposed to strengthen the expression of causal regions. Subsequently, regularized intra-class compactness loss (RIC-Loss) and intra-class relationship aware consistency loss (IRC-Loss) are devised to optimize the embedding space. In conjunction with joint supervised training using cross-entropy loss, RCS-OSR can effectively reduce empirical classification risk and open space risk simultaneously. Moreover, a class-aware OSR classifier with adaptive thresholding is designed to leverage the differences between different classes. Consequently, our method can construct distinct decision boundaries between known and unknown classes to simultaneously classify known classes and identify unknown classes in open scenarios. Extensive experiments conducted on the MSTAR dataset demonstrate the effectiveness and superiority of our method in various OSR tasks. Full article
(This article belongs to the Special Issue Target Recognition and Detection Based on High Resolution Radar Images)
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20 pages, 3843 KiB  
Article
Open-Set Recognition Model for SAR Target Based on Capsule Network with the KLD
by Chunyun Jiang, Huiqiang Zhang, Ronghui Zhan, Wenyu Shu and Jun Zhang
Remote Sens. 2024, 16(17), 3141; https://doi.org/10.3390/rs16173141 - 26 Aug 2024
Cited by 1 | Viewed by 722
Abstract
Synthetic aperture radar (SAR) automatic target recognition (ATR) technology has seen significant advancements. Despite these advancements, the majority of research still operates under the closed-set assumption, wherein all test samples belong to classes seen during the training phase. In real-world applications, however, it [...] Read more.
Synthetic aperture radar (SAR) automatic target recognition (ATR) technology has seen significant advancements. Despite these advancements, the majority of research still operates under the closed-set assumption, wherein all test samples belong to classes seen during the training phase. In real-world applications, however, it is common to encounter targets not previously seen during training, posing a significant challenge to the existing methods. Ideally, an ATR system should not only accurately identify known target classes but also effectively reject those belonging to unknown classes, giving rise to the concept of open set recognition (OSR). To address this challenge, we propose a novel approach that leverages the unique capabilities of the Capsule Network and the Kullback-Leibler divergence (KLD) to distinguish unknown classes. This method begins by deeply mining the features of SAR targets using the Capsule Network and enhancing the separability between different features through a specially designed loss function. Subsequently, the KLD of features between a testing sample and the center of each known class is calculated. If the testing sample exhibits a significantly larger KLD compared to all known classes, it is classified as an unknown target. The experimental results of the SAR-ACD dataset demonstrate that our method can maintain a correct identification rate of over 95% for known classes while effectively recognizing unknown classes. Compared to existing techniques, our method exhibits significant improvements. Full article
(This article belongs to the Special Issue Target Recognition and Detection Based on High Resolution Radar Images)
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24 pages, 1214 KiB  
Article
An Interpretable Target-Aware Vision Transformer for Polarimetric HRRP Target Recognition with a Novel Attention Loss
by Fan Gao, Ping Lang, Chunmao Yeh, Zhangfeng Li, Dawei Ren and Jian Yang
Remote Sens. 2024, 16(17), 3135; https://doi.org/10.3390/rs16173135 - 25 Aug 2024
Cited by 1 | Viewed by 944
Abstract
Polarimetric high-resolution range profile (HRRP), with its rich polarimetric and spatial information, has become increasingly important in radar automatic target recognition (RATR). This study proposes an interpretable target-aware vision Transformer (ITAViT) for polarimetric HRRP target recognition with a novel attention loss. In ITAViT, [...] Read more.
Polarimetric high-resolution range profile (HRRP), with its rich polarimetric and spatial information, has become increasingly important in radar automatic target recognition (RATR). This study proposes an interpretable target-aware vision Transformer (ITAViT) for polarimetric HRRP target recognition with a novel attention loss. In ITAViT, we initially fuse the polarimetric features and the amplitude of polarimetric HRRP with a polarimetric preprocessing layer (PPL) to obtain the feature map as the input of the subsequent network. The vision Transformer (ViT) is then used as the backbone to automatically extract both local and global features. Most importantly, we introduce a novel attention loss to optimize the alignment between the attention map and the HRRP span. Thus, it can improve the difference between the target and the background, and enable the model to more effectively focus on real target areas. Experiments on a simulated X-band dataset demonstrate that our proposed ITAViT outperforms comparative models under various experimental conditions. Ablation studies highlight the effectiveness of polarimetric preprocessing and attention loss. Furthermore, the visualization of the self-attention mechanism suggests that attention loss enhances the interpretability of the network. Full article
(This article belongs to the Special Issue Target Recognition and Detection Based on High Resolution Radar Images)
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18 pages, 3295 KiB  
Article
Realizing Small UAV Targets Recognition via Multi-Dimensional Feature Fusion of High-Resolution Radar
by Wen Jiang, Zhen Liu, Yanping Wang, Yun Lin, Yang Li and Fukun Bi
Remote Sens. 2024, 16(15), 2710; https://doi.org/10.3390/rs16152710 - 24 Jul 2024
Viewed by 1198
Abstract
For modern radar systems, small unmanned aerial vehicles (UAVs) belong to a typical types of targets with ‘low, slow, and small’ characteristics. In complex combat environments, the functional requirements of radar systems are not only limited to achieving stable detection and tracking performance [...] Read more.
For modern radar systems, small unmanned aerial vehicles (UAVs) belong to a typical types of targets with ‘low, slow, and small’ characteristics. In complex combat environments, the functional requirements of radar systems are not only limited to achieving stable detection and tracking performance but also to effectively complete the recognition of small UAV targets. In this paper, a multi-dimensional feature fusion framework for small UAV target recognition utilizing a small-sized and low-cost high-resolution radar is proposed, which can fully extract and combine the geometric structure features and the micro-motion features of small UAV targets. For the performance analysis, the echo data of different small UAV targets was measured and collected with a millimeter-wave radar, and the dataset consists of high-resolution range profiles (HRRP) and micro-Doppler time–frequency spectrograms was constructed for training and testing. The effectiveness of the proposed method was demonstrated by a series of comparison experiments, and the overall accuracy of the proposed method can reach 98.5%, which demonstrates that the proposed multi-dimensional feature fusion method can achieve better recognition performance than that of classical algorithms and higher robustness than that of single features for small UAV targets. Full article
(This article belongs to the Special Issue Target Recognition and Detection Based on High Resolution Radar Images)
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