Volume 14 Issue 2
Apr.  2025
Turn off MathJax
Article Contents
Abstract
References
Article Navigation >  Journal of Radars  > 2025  >  14(2): 366-388
YANG Lichao, GAO Yuexin, XING Mengdao, et al. High resolution microwave photonics radar real-time imaging based on generalized keystone and frequency scaling[J]. Journal of Radars, 2019, 8(2): 215–223. doi: 10.12000/JR18120
Citation: CAO Jingyi, ZHANG Yang, YOU Ya’nan, et al. Target recognition method based on graph structure perception of invariant features for SAR images[J]. Journal of Radars, 2025, 14(2): 366–388. doi: 10.12000/JR24125
shu

Target Recognition Method Based on Graph Structure Perception of Invariant Features for SAR Images

DOI: 10.12000/JR24125 CSTR: 32380.14.JR24125
  • 1.

    School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China

  • 2.

    School of Electronic and Information Engineering, Beihang University, Beijing 100191, China

  • 3.

    Changsha Tianyi Space Science and Technology Research Institute Co., Ltd., Changsha 410221, China

Funds:  The National Key Research and Development Program of China (2023YFC3305901), The National Natural Science Foundation of China (62101060)
More Information
  • Corresponding author: YOU Ya’nan, youyanan@bupt.edu.cn
  • Received Date: 2024-06-19
  • Rev Recd Date: 2024-12-25
    • Available Online: 2024-12-27
  • Publish Date: 2025-01-13
    Abstract
  • Synthetic Aperture Radar (SAR) image target recognition technology based on deep learning has matured. However, challenges remain due to scattering phenomenon and noise interference that cause significant intraclass variability in imaging results. Invariant features, which represent the essential attributes of a specific target class with consistent expressions, are crucial for high-precision recognition. We define these invariant features from the entity, its surrounding environment, and their combined context as the target’s essential features. Guided by multilevel essential feature modeling theory, we propose a SAR image target recognition method based on graph networks and invariant feature perception. This method employs a dual-branch network to process multiview SAR images simultaneously using a rotation-learnable unit to adaptively align dual-branch features and reinforce invariant features with rotational immunity by minimizing intraclass feature differences. Specifically, to support essential feature extraction in each branch, we design a feature-guided graph feature perception module based on multilevel essential feature modeling. This module uses salient points for target feature analysis and comprises a target ontology feature enhancement unit, an environment feature sampling unit, and a context-based adaptive fusion update unit. Outputs are analyzed with a graph neural network and constructed into a topological representation of essential features, resulting in a target category vector. The t-Distributed Stochastic Neighbor Embedding (t-SNE) method is used to qualitatively evaluate the algorithm’s classification ability, while metrics like accuracy, recall, and F1 score are used to quantitatively analyze key units and overall network performance. Additionally, class activation map visualization methods are employed to validate the extraction and analysis of invariant features at different stages and branches. The proposed method achieves recognition accuracies of 98.56% on the MSTAR dataset, 94.11% on SAR-ACD dataset, and 86.20% on OpenSARShip dataset, demonstrating its effectiveness in extracting essential target features.

     

    • FullText(HTML)
    • References(59)
  • [1]
    YU P, QIN Kai, and CLAUSI D A. Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4): 1302–1317. doi: 10.1109/TGRS.2011.2164085.
    [2]
    SHANG Ronghua, LIU Mengmeng, JIAO Licheng, et al. Region-level SAR image segmentation based on edge feature and label assistance[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5237216. doi: 10.1109/TGRS.2022.3217053.
    [3]
    GAO Gui. Statistical modeling of SAR images: A survey[J]. Sensors, 2010, 10(1): 775–795. doi: 10.3390/s100100775.
    [4]
    QIN Xianxiang, ZOU Huanxin, ZHOU Shilin, et al. Region-based classification of SAR images using kullback-leibler distance between generalized gamma distributions[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(8): 1655–1659. doi: 10.1109/LGRS.2015.2418217.
    [5]
    LOPES A, LAUR H, and NEZRY E. Statistical distribution and texture in multilook and complex sar images[C]. The 10th Annual International Symposium on Geoscience and Remote Sensing, College Park, MD, USA, 1990: 2427–2430. doi: 10.1109/IGARSS.1990.689030.
    [6]
    MENON M V. Estimation of the shape and scale parameters of the weibull distribution[J]. Technometrics, 1963, 5(2): 175–182. doi: 10.1080/00401706.1963.10490073.
    [7]
    DU Peijun, SAMAT A, WASKE B, et al. Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 105: 38–53. doi: 10.1016/j.isprsjprs.2015.03.002.
    [8]
    PARIKH H, PATEL S, and PATEL V. Classification of SAR and PolSAR images using deep learning: A review[J]. International Journal of Image and Data Fusion, 2020, 11(1): 1–32. doi: 10.1080/19479832.2019.1655489.
    [9]
    张翼鹏, 卢东东, 仇晓兰, 等. 基于散射点拓扑和双分支卷积神经网络的SAR图像小样本舰船分类[J]. 雷达学报(中英文), 2024, 13(2): 411–427. doi: 10.12000/JR23172.

    ZHANG Yipeng, LU Dongdong, QIU Xiaolan, et al. Few-shot ship classification of SAR images via scattering point topology and dual-branch convolutional neural network[J]. Journal of Radars, 2024, 13(2): 411–427. doi: 10.12000/JR23172.
    [10]
    LIU Fang, JIAO Licheng, HOU Biao, et al. POL-SAR image classification based on wishart DBN and local spatial information[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(6): 3292–3308. doi: 10.1109/TGRS.2016.2514504.
    [11]
    GENG Jie, FAN Jianchao, WANG Hongyu, et al. High-resolution SAR image classification via deep convolutional autoencoders[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(11): 2351–2355. doi: 10.1109/LGRS.2015.2478256.
    [12]
    ZHU Yimin, YUAN Kexin, ZHONG Wenlong, et al. Spatial-spectral ConvNeXt for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 5453–5463. doi: 10.1109/JSTARS.2023.3282975.
    [13]
    DAS A and CHANDRAN S. Transfer learning with Res2Net for remote sensing scene classification[C]. 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2021: 796–801. doi: 10.1109/Confluence51648.2021.9377148.
    [14]
    NIU Chaoyang, GAO Ouyang, LU Wanjie, et al. Reg-SA-UNet++: A lightweight landslide detection network based on single-temporal images captured postlandslide[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 9746–9759. doi: 10.1109/JSTARS.2022.3219897.
    [15]
    SIVASUBRAMANIAN A, VR P, HARI T, et al. Transformer-based convolutional neural network approach for remote sensing natural scene classification[J]. Remote Sensing Applications: Society and Environment, 2024, 33: 101126. doi: 10.1016/j.rsase.2023.101126.
    [16]
    WANG Haitao, CHEN Jie, HUANG Zhixiang, et al. FPT: Fine-grained detection of driver distraction based on the feature pyramid vision transformer[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 24(2): 1594–1608. doi: 10.1109/TITS.2022.3219676.
    [17]
    DAI Yuqi, ZHENG Tie, XUE Changbin, et al. SegMarsViT: Lightweight mars terrain segmentation network for autonomous driving in planetary exploration[J]. Remote Sensing, 2022, 14(24): 6297. doi: 10.3390/rs14246297.
    [18]
    CHEN Yihan, GU Xingyu, LIU Zhen, et al. A fast inference vision transformer for automatic pavement image classification and its visual interpretation method[J]. Remote Sensing, 2022, 14(8): 1877. doi: 10.3390/rs14081877.
    [19]
    HUAN Hai and ZHANG Bo. FDAENet: Frequency domain attention encoder-decoder network for road extraction of remote sensing images[J]. Journal of Applied Remote Sensing, 2024, 18(2): 024510. doi: 10.1117/1.JRS.18.024510.
    [20]
    ZHANG Si, TONG Hanghang, XU Jiejun, et al. Graph convolutional networks: A comprehensive review[J]. Computational Social Networks, 2019, 6(1): 11. doi: 10.1186/s40649-019-0069-y.
    [21]
    HAMILTON W L, YING R, and LESKOVEC J. Inductive representation learning on large graphs[C]. The 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA, 2017: 1025–1035.
    [22]
    LIU Chuang, ZHAN Yibing, WU Jia, et al. Graph pooling for graph neural networks: Progress, challenges, and opportunities[C]. The Thirty-Second International Joint Conference on Artificial Intelligence, Macao, China, 2023: 6712–6722. doi: 10.24963/ijcai.2023/752.
    [23]
    VELIČKOVIĆ P, CUCURULL G, CASANOVA A, et al. Graph attention networks[C]. The Sixth International Conference on Learning Representation, Vancouver, BC, Canada, 2018: 1–12.
    [24]
    OVEIS A H, GIUSTI E, GHIO S, et al. A survey on the applications of convolutional neural networks for synthetic aperture radar: Recent advances[J]. IEEE Aerospace and Electronic Systems Magazine, 2022, 37(5): 18–42. doi: 10.1109/MAES.2021.3117369.
    [25]
    WANG Ru, NIE Yinju, and GENG Jie. Multiscale superpixel-guided weighted graph convolutional network for polarimetric SAR image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 3727–3741. doi: 10.1109/JSTARS.2024.3355290.
    [26]
    ZHANG Yawei, CAO Yu, FENG Xubin, et al. SAR object detection encounters deformed complex scenes and aliased scattered power distribution[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 4482–4495. doi: 10.1109/JSTARS.2022.3157749.
    [27]
    WANG Siyuan, WANG Yinghua, LIU Hongwei, et al. A few-shot SAR target recognition method by unifying local classification with feature generation and calibration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5200319. doi: 10.1109/TGRS.2023.3337856.
    [28]
    FENG Yunxiang, YOU Yanan, TIAN Jing, et al. OEGR-DETR: A novel detection transformer based on orientation enhancement and group relations for SAR object detection[J]. Remote Sensing, 2023, 16(1): 106. doi: 10.3390/rs16010106.
    [29]
    吕艺璇, 王智睿, 王佩瑾, 等. 基于散射信息和元学习的SAR图像飞机目标识别[J]. 雷达学报, 2022, 11(4): 652–665. doi: 10.12000/JR22044.

    LYU Yixuan, WANG Zhirui, WANG Peijin, et al. Scattering information and meta-learning based SAR images interpretation for aircraft target recognition[J]. Journal of Radars, 2022, 11(4): 652–665. doi: 10.12000/JR22044.
    [30]
    张睿, 王梓祺, 李阳, 等. 任务感知的多尺度小样本SAR图像分类方法[J]. 计算机科学, 2024, 51(8): 160–167. doi: 10.11896/jsjkx.230500171.

    ZHANG Rui, WANG Ziqi, LI Yang, et al. Task-aware few-shot SAR image classification method based on multi-scale attention mechanism[J]. Computer Science, 2024, 51(8): 160–167. doi: 10.11896/jsjkx.230500171.
    [31]
    史宝岱, 张秦, 李宇环, 等. 基于混合注意力机制的SAR图像目标识别算法[J]. 电光与控制, 2023, 30(4): 45–49. doi: 10.3969/j.issn.1671-637X.2023.04.009.

    SHI Baodai, ZHANG Qin, LI Yuhuan, et al. SAR image target recognition based on hybrid attention mechanism[J]. Electronics Optics & Control, 2023, 30(4): 45–49. doi: 10.3969/j.issn.1671-637X.2023.04.009.
    [32]
    罗汝, 赵凌君, 何奇山, 等. SAR图像飞机目标智能检测识别技术研究进展与展望[J]. 雷达学报(中英文), 2024, 13(2): 307–330. doi: 10.12000/JR23056.

    LUO Ru, ZHAO Lingjun, HE Qishan, et al. Intelligent technology for aircraft detection and recognition through SAR imagery: Advancements and prospects[J]. Journal of Radars, 2024, 13(2): 307–330. doi: 10.12000/JR23056.
    [33]
    KANG Yuzhuo, WANG Zhirui, ZUO Haoyu, et al. ST-Net: Scattering topology network for aircraft classification in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5202117. doi: 10.1109/TGRS.2023.3236987.
    [34]
    YI Yonghao, YOU Yanan, LI Chao, et al. EFM-Net: An essential feature mining network for target fine-grained classification in optical remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5606416. doi: 10.1109/TGRS.2023.3265669.
    [35]
    文贡坚, 马聪慧, 丁柏圆, 等. 基于部件级三维参数化电磁模型的SAR目标物理可解释识别方法[J]. 雷达学报, 2020, 9(4): 608–621. doi: 10.12000/JR20099.

    WEN Gongjian, MA Conghui, DING Baiyuan, et al. SAR target physics interpretable recognition method based on three dimensional parametric electromagnetic part model[J]. Journal of Radars, 2020, 9(4): 608–621. doi: 10.12000/JR20099.
    [36]
    POTTER L C and MOSES R L. Attributed scattering centers for SAR ATR[J]. IEEE Transactions on Image Processing, 1997, 6(1): 79–91. doi: 10.1109/83.552098.
    [37]
    DATCU M, HUANG Zhongling, ANGHEL A, et al. Explainable, physics-aware, trustworthy artificial intelligence: A paradigm shift for synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Magazine, 2023, 11(1): 8–25. doi: 10.1109/MGRS.2023.3237465.
    [38]
    朱凯雯, 尤亚楠, 曹婧宜, 等. Hybrid-Gird: 遥感图像细粒度分类可解释方法[J]. 遥感学报, 2024, 28(7): 1722–1734. doi: 10.11834/jrs.20243252.

    ZHU Kaiwen, YOU Yanan, CAO Jingyi, et al. Hybrid-Gird: An explainable method for fine-grained classification of remote sensing images[J]. National Remote Sensing Bulletin, 2024, 28(7): 1722–1734. doi: 10.11834/jrs.20243252.
    [39]
    DENG Huiqi, ZOU Na, DU Mengnan, et al. Unifying fourteen post-hoc attribution methods with taylor interactions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(7): 4625–4640. doi: 10.1109/TPAMI.2024.3358410.
    [40]
    丁柏圆, 文贡坚, 余连生, 等. 属性散射中心匹配及其在SAR目标识别中的应用[J]. 雷达学报, 2017, 6(2): 157–166. doi: 10.12000/JR16104.

    DING Baiyuan, WEN Gongjian, YU Liansheng, et al. Matching of attributed scattering center and its application to synthetic aperture radar automatic target recognition[J]. Journal of Radars, 2017, 6(2): 157–166. doi: 10.12000/JR16104.
    [41]
    黄钟泠, 姚西文, 韩军伟. 面向SAR图像解译的物理可解释深度学习技术进展与探讨[J]. 雷达学报, 2022, 11(1): 107–125. doi: 10.12000/JR21165.

    HUANG Zhongling, YAO Xiwen, and HAN Junwei. Progress and perspective on physically explainable deep learning for synthetic aperture radar image interpretation[J]. Journal of Radars, 2022, 11(1): 107–125. doi: 10.12000/JR21165.
    [42]
    FENG Sijia, JI Kefeng, WANG Fulai, et al. Electromagnetic Scattering Feature (ESF) module embedded network based on ASC model for robust and interpretable SAR ATR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5235415. doi: 10.1109/TGRS.2022.3208333.
    [43]
    ZHANG Chen, WANG Yinghua, LIU Hongwei, et al. VSFA: Visual and scattering topological feature fusion and alignment network for unsupervised domain adaptation in SAR target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5216920. doi: 10.1109/TGRS.2023.3317828.
    [44]
    WANG Ruiqiu, SU Tao, XU Dan, et al. MIGA-Net: Multi-view image information learning based on graph attention network for SAR target recognition[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2024, 34(11): 10779–10792. doi: 10.1109/TCSVT.2024.3418979.
    [45]
    JADERBERG M, SIMONYAN K, ZISSERMAN A, et al. Spatial transformer networks[C]. The 28th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015: 2017–2025.
    [46]
    KEYDEL E R, LEE S W, and MOORE J T. MSTAR extended operating conditions: A tutorial[C]. The SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, Orlando, FL, US, 1996: 228–242. doi: 10.1117/12.242059.
    [47]
    SUN Xian, LV Yixuan, WANG Zhirui, et al. SCAN: Scattering characteristics analysis network for few-shot aircraft classification in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5226517. doi: 10.1109/TGRS.2022.3166174.
    [48]
    ZHAO Juanping, ZHANG Zenghui, YAO Wei, et al. OpenSARUrban: A sentinel-1 SAR image dataset for urban interpretation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 187–203. doi: 10.1109/JSTARS.2019.2954850.
    [49]
    ZHOU Xiaoqian, LUO Cai, REN Peng, et al. Multiscale complex-valued feature attention convolutional neural network for SAR automatic target recognition[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 2052–2066. doi: 10.1109/JSTARS.2023.3342986.
    [50]
    JIANG Chunyun, ZHANG Huiqiang, ZHAN Ronghui, et al. Open-set recognition model for SAR target based on capsule network with the KLD[J]. Remote Sensing, 2024, 16(17): 3141. doi: 10.3390/rs16173141.
    [51]
    ZHANG Tong, TONG Xiaobao, and WANG Yong. Semantics-assisted multiview fusion for SAR automatic target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 4007005. doi: 10.1109/LGRS.2024.3374375.
    [52]
    SIMONYAN K and ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https://arxiv.org/abs/1409.1556, 2015.
    [53]
    HE Kaiming, ZHANG Xiaohu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016: 770–778. doi: 10.1109/CVPR.2016.90.
    [54]
    SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016: 2818–2826. doi: 10.1109/CVPR.2016.308.
    [55]
    LIU Ze, LIN Yutong, CAO Yue, et al. Swin transformer: Hierarchical vision transformer using shifted windows[C]. 2021 IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 2021: 9992–10002. doi: 10.1109/ICCV48922.2021.00986.
    [56]
    李妙歌, 陈渤, 王东升, 等. 面向SAR图像目标分类的CNN模型可视化方法[J]. 雷达学报(中英文), 2024, 13(2): 359–373. doi: 10.12000/JR23107.

    LI Miaoge, CHEN Bo, WANG Dongsheng, et al. CNN model visualization method for SAR image target classification[J]. Journal of Radars, 2024, 13(2): 359–373. doi: 10.12000/JR23107.
    [57]
    CHEN Hongkai, LUO Zixin, ZHANG Jiahui, et al. Learning to match features with seeded graph matching network[C]. 2021 IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 2021: 6281–6290. doi: 10.1109/ICCV48922.2021.00624.
    [58]
    LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91–110. doi: 10.1023/B:VISI.0000029664.99615.94.
    [59]
    RADFORD A, KIM J W, HALLACY C, et al. Learning transferable visual models from natural language supervision[EB/OL]. https://arxiv.org/abs/2103.00020, 2021.
    • Relative Articles

  • Relative Articles

    [1]LIU Deshun, XIA Deping, CHEN Lu, MA Yanfeng. Joint Design of LPI Transmit Waveform and Receive Beamforming Based on Neural Networks for FDA-MIMO[J]. Journal of Radars, 2024, 13(6): 1239-1251. doi: 10.12000/JR24140
    [2]TIAN Ye, DING Chibiao, ZHANG Fubo, SHI Min’an. SAR Building Area Layover Detection Based on Deep Learning[J]. Journal of Radars, 2023, 12(2): 441-455. doi: 10.12000/JR23033
    [3]LI Zhengjie, XIE Junwei, ZHANG Haowei, WEN Quan, LIU Bin. A Fast Power Allocation Algorithm in a Collocated MIMO Radar under Low Interception Backgrounds[J]. Journal of Radars, 2023, 12(3): 602-615. doi: 10.12000/JR22203
    [4]WAN Huan, YU Xianxiang, QUAN Zhi, LIAO Bin. Constant Modulus Waveform Design for Low-resolution Quantization MIMO Radar Based on an Alternating Direction Penalty Method[J]. Journal of Radars, 2022, 11(4): 557-569. doi: 10.12000/JR22072
    [5]YAO Yu, LI Zeqing, FAN Wen, DU Xiaolin, WU Lenan. Spectrally Compatible Waveform Design for MIMO Radar Based on ABSUM Method[J]. Journal of Radars, 2022, 11(4): 543-556. doi: 10.12000/JR22138
    [6]FAN Wen, YU Baoguo, CHEN Jing, ZHANG Hang, LI Chunze. Joint Waveform Optimization and Antenna Position Selection for MIMO Radar Beam Scanning[J]. Journal of Radars, 2022, 11(4): 530-542. doi: 10.12000/JR22135
    [7]ZHENG Guimei, SONG Yuwei, HU Guoping, LI Binbin, ZHANG Dong. Height Measurement for Meter-wave MIMO Radar Based on Block Orthogonal Matching Pursuit Preprocessing[J]. Journal of Radars, 2020, 9(5): 908-915. doi: 10.12000/JR20042
    [8]ZHANG Jinsong, XING Mengdao, SUN Guangcai. A Water Segmentation Algorithm for SAR Image Based on Dense Depthwise Separable Convolution[J]. Journal of Radars, 2019, 8(3): 400-412. doi: 10.12000/JR19008
    [9]ZHAO Xianbin, YAN Wei, AI Weihua, LU Wen, MA Shuo. Research on Calculation Method of Doppler Centroid Shift from Airborne Synthetic Aperture Radar for Ocean Feature Retrieval[J]. Journal of Radars, 2019, 8(3): 391-399. doi: 10.12000/JR19020
    [10]Wang Jie, Ding Chibiao, Liang Xingdong, Chen Longyong, Qi Zhimei. Research Outline of Airborne MIMO-SAR System with Same Time-frequency Coverage[J]. Journal of Radars, 2018, 7(2): 220-234. doi: 10.12000/JR17046
    [11]Wang Pei, Sun Huifeng, Yu Weidong. A Novel Wireless Internal Calibration Method of Spaceborne SAR[J]. Journal of Radars, 2018, 7(4): 425-436. doi: 10.12000/JR18005
    [12]Xu Zhen, Wang Robert, Li Ning, Zhang Heng, Zhang Lei. A Novel Approach to Change Detection in SAR Images with CNN Classification[J]. Journal of Radars, 2017, 6(5): 483-491. doi: 10.12000/JR17075
    [13]Zhao Junxiang, Liang Xingdong, Li Yanlei. Change Detection in SAR CCD Based on the Likelihood Change Statistics[J]. Journal of Radars, 2017, 6(2): 186-194. doi: 10.12000/JR16065
    [14]Wang Fulai, Pang Chen, Li Yongzhen, Wang Xuesong. Orthogonal Polyphase Coded Waveform Design Method for Simultaneous Fully Polarimetric Radar[J]. Journal of Radars, 2017, 6(4): 340-348. doi: 10.12000/JR16150
    [15]Guo Zhen-yu, Lin Yun, Hong Wen. A Focusing Algorithm for Circular SAR Based on Phase Error Estimation in Image Domain[J]. Journal of Radars, 2015, 4(6): 681-688. doi: 10.12000/JR15046
    [16]Jiang Hai, Song Hong-jun. Improved MISO-SAR System Based on BiDirectional Imaging[J]. Journal of Radars, 2015, 4(5): 571-581. doi: 10.12000/JR15022
    [17]Zhe Xiao-qiang, Chou Xiao-lan, Han Bing, Lei Bin. An Improved Doppler Rate Estimation Approach for Sliding Spotlight SAR Data Based on the Transposition Domain[J]. Journal of Radars, 2014, 3(4): 419-427. doi: 10.3724/SP.J.1300.2014.14008
    [18]Gao Yang, Yu Wei-dong, Feng Jin, Zheng Shi-chao, Yang Liang. A SAR Back Projection Autofocusing Algorithm Based on Legendre Approximation[J]. Journal of Radars, 2014, 3(2): 176-182. doi: 10.3724/SP.J.1300.2014.14011
    [19]Meng Da-di, Hu Yu-xin, Ding Chi-biao. An Efficient Algorithm to Processing SAR Data on GPU[J]. Journal of Radars, 2013, 2(2): 210-217. doi: 10.3724/SP.J.1300.2013.20098
    [20]Li Fang-fang, Zhan Yi, Hu Dong-hui, Ding Chi-biao. A Fast Method for InSAR Phase Unwrapping Based on Quality Guide[J]. Journal of Radars, 2012, 1(2): 196-202. doi: 10.3724/SP.J.1300.2012.20023
    • Supplements(0)
    • Cited By

  • Cited by

    Periodical cited type(8)

    1. 白杨，殷红成，黄培康，刘芳. 基于宽带极化纯度估计的极化测量定标修正. 系统工程与电子技术. 2024(02): 428-436 .
    2. 李泽榕，杨勇. 基于X波段无人机暗室测量数据的雷达探测性能分析. 信息对抗技术. 2023(06): 61-70 .
    3. 李郝亮，陈思伟. 极化测量误差对人造目标散射解译性能的影响研究. 现代雷达. 2022(01): 1-8 .
    4. 白杨，侯鑫，刘芳，殷红成. 基于宽带相位修正的散射矩阵变极化基测量. 系统工程与电子技术. 2022(02): 506-511 .
    5. 杨勇，王雪松，张斌. 基于时频检测与极化匹配的雷达无人机检测方法. 电子与信息学报. 2021(03): 509-515 .
    6. 张斌，杨勇，逯旺旺，王雪松，肖顺平. Ku波段固定翼无人机全极化RCS统计特性研究. 现代雷达. 2020(06): 41-47 .
    7. 王雪松，杨勇. 海杂波与目标极化特性研究进展. 电波科学学报. 2019(06): 665-675 .
    8. 章鹏飞，李刚，霍超颖，殷红成. 基于双雷达微动特征融合的无人机分类识别. 雷达学报. 2018(05): 557-564 . 本站查看

    Other cited types(11)

  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(389) PDF downloads(94) Cited by(19)
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return