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YANG Huizhang, XI Feng, LIU Zhong, et al. Sub-band contrast and entropy of SAR images: Concepts and applications to interference detection and suppression[J]. Journal of Radars, in press. doi: 10.12000/JR25027
Citation: YANG Huizhang, XI Feng, LIU Zhong, et al. Sub-band contrast and entropy of SAR images: Concepts and applications to interference detection and suppression[J]. Journal of Radars, in press. doi: 10.12000/JR25027
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Sub-band Contrast and Entropy of SAR Images: Concepts and Applications to Interference Detection and Suppression

DOI: 10.12000/JR25027
  • 1.

    School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

  • 2.

    Nanjing Research Institute of Electronics Technology, Nanjing 210039, China

  • 3.

    Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Funds:  The National Natural Science Foundation of China (62301259, 62171224), The Fundamental Research Funds for the Central Universities (30924010914)
More Information
  • Corresponding author: YANG Huizhang, hzyang@njust.edu.cn
  • Received Date: 2025-02-12
  • Rev Recd Date: 2025-05-26
    • Available Online: 2025-06-05
    Abstract
  • Spaceborne Synthetic Aperture Radar (SAR) data may be prone to interrupted-sampling repeater jamming and many common unintentional interferences, such as linear frequency modulated pulses. In this paper, we first divide a single-look complex SAR image into multiple sub-band images of equal bandwidth in the range frequency domain. Then, we model the pixel intensity of these sub-band images and analyze the fluctuation mechanism of interfering and noninterfering pixels across the sub-bands. The findings reveal that the energy distribution of interfering pixels is uneven across different sub-bands, leading to substantial intensity fluctuations within the sub-band domain, whereas the intensity of noninterfering pixels remains relatively stable. Based on this observation, we define sub-band contrast and sub-band entropy as statistical measures to quantify fluctuation characteristics across the sub-bands. These measures are then compared with certain thresholds to obtain detection results. Statistical analysis revealed that under noninterfering conditions, these two statistics approximately follow the beta distribution. By leveraging this finding, we fit the distributions of these measures using the beta distribution and develop a method to determine detection thresholds under the constant-false-alarm-rate criterion. Experimental results showed that the proposed method can effectively detect interrupted-sampling repeater jamming and common unintentional interferences. In addition, we investigated the impact of the jamming-to-signal ratio on detection performance and verified the reliability and stability of the method via Monte Carlo simulations. Furthermore, we introduced an interference suppression technique based on a rank-1 model to reduce the adverse effects of interference on downstream tasks. This technique is capable of adaptively suppressing interference in detected regions.

     

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    • References(71)
  • [1]
    李永祯, 黄大通, 邢世其, 等. 合成孔径雷达干扰技术研究综述[J]. 雷达学报, 2020, 9(5): 753–764. doi: 10.12000/JR20087.

    LI Yongzhen, HUANG Datong, XING Shiqi, et al. A review of synthetic aperture radar jamming technique[J]. Journal of Radars, 2020, 9(5): 753–764. doi: 10.12000/JR20087.
    [2]
    吴晓芳, 代大海, 王雪松, 等. 合成孔径雷达电子对抗技术综述[J]. 信号处理, 2010, 26(3): 424–435. doi: 10.3969/j.issn.1003-0530.2010.03.017.

    WU Xiaofang, DAI Dahai, WANG Xuesong, et al. Review of synthetic aperture radar electronic countermeasures[J]. Signal Processing, 2010, 26(3): 424–435. doi: 10.3969/j.issn.1003-0530.2010.03.017.
    [3]
    陈思伟, 崔兴超, 李铭典, 等. 基于深度CNN模型的SAR图像有源干扰类型识别方法[J]. 雷达学报, 2022, 11(5): 897–908. doi: 10.12000/JR22143.

    CHEN Siwei, CUI Xingchao, LI Mingdian, et al. SAR image active jamming type recognition based on deep CNN model[J]. Journal of Radars, 2022, 11(5): 897–908. doi: 10.12000/JR22143.
    [4]
    李兵, 洪文. 合成孔径雷达噪声干扰研究[J]. 电子学报, 2004, 32(12): 2035–2037. doi: 10.3321/j.issn:0372-2112.2004.12.025.

    LI Bing and HONG Wen. Study of noise jamming to SAR[J]. Acta Electronica Sinica, 2004, 32(12): 2035–2037. doi: 10.3321/j.issn:0372-2112.2004.12.025.
    [5]
    吕波, 冯起, 张晓发, 等. 对SAR的随机脉冲卷积干扰研究[J]. 中国电子科学研究院学报, 2008, 3(3): 276–279. doi: 10.3969/j.issn.1673-5692.2008.03.011.

    LV Bo, FENG Qi, ZHANG Xiaofa, et al. Study of random pulse convolution jamming to SAR[J]. Journal of China Academy of Electronics and Information Technology, 2008, 3(3): 276–279. doi: 10.3969/j.issn.1673-5692.2008.03.011.
    [6]
    黄洪旭, 黄知涛, 周一宇. 对合成孔径雷达的移频干扰研究[J]. 宇航学报, 2006, 27(3): 463–468. doi: 10.3321/j.issn:1000-1328.2006.03.027.

    HUANG Hongxu, HUANG Zhitao, and ZHOU Yiyu. A study on the shift-frequency jamming to SAR[J]. Journal of Astronautics, 2006, 27(3): 463–468. doi: 10.3321/j.issn:1000-1328.2006.03.027.
    [7]
    黄洪旭, 黄知涛, 周一宇. 对合成孔径雷达的随机移频干扰[J]. 信号处理, 2007, 23(1): 41–45. doi: 10.3969/j.issn.1003-0530.2007.01.009.

    HUANG Hongxu, HUANG Zhitao, and ZHOU Yiyu. Randomly-shift-frequency jamming style to SAR[J]. Signal Processing, 2007, 23(1): 41–45. doi: 10.3969/j.issn.1003-0530.2007.01.009.
    [8]
    黄洪旭, 黄知涛, 吴京, 等. 对合成孔径雷达的步进移频干扰[J]. 宇航学报, 2011, 32(4): 898–902. doi: 10.3873/j.issn.1000-1328.2011.04.028.

    HUANG Hongxu, HUANG Zhitao, WU Jing, et al. Stepped-shift-frequency jamming to SAR[J]. Journal of Astronautics, 2011, 32(4): 898–902. doi: 10.3873/j.issn.1000-1328.2011.04.028.
    [9]
    刘玉玲. SAR有源假目标精确位置欺骗干扰技术研究[D]. [硕士论文], 国防科学技术大学, 2012.

    LIU Yuling. Research on SAR precisely position deception jamming[D]. [Master dissertation], National University of Defense Technolog, 2012.
    [10]
    吴晓芳, 代大海, 王雪松, 等. 基于微动调制的SAR新型有源干扰方法[J]. 电子学报, 2010, 38(4): 954–959.

    WU Xiaofang, DAI Dahai, WANG Xuesong, et al. A novel method of active jamming for SAR based on micro motion modulation[J]. Acta Electronica Sinica, 2010, 38(4): 954–959.
    [11]
    吴晓芳, 王雪松, 梁景修. SAR-GMTI高逼真匀速运动假目标调制干扰方法[J]. 宇航学报, 2012, 33(10): 1472–1479. doi: 10.3873/j.issn.1000-1328.2012.10.016.

    WU Xiaofang, WANG Xuesong, and LIANG Jingxiu. Modulation jamming method for high-vivid false uniformly-moving targets against SAR-GMTI[J]. Journal of Astronautics, 2012, 33(10): 1472–1479. doi: 10.3873/j.issn.1000-1328.2012.10.016.
    [12]
    王雪松, 刘建成, 张文明, 等. 间歇采样转发干扰的数学原理[J]. 中国科学E辑 信息科学, 2006, 36(8): 891–901. doi: 10.3969/j.issn.1674-7259.2006.08.007.

    WANG Xuesong, LIU Jiancheng, ZHANG Wenming, et al. Mathematical principle of intermittent sampling and forwarding interference[J]. Science in China (Series E), 2006, 36(8): 891–901. doi: 10.3969/j.issn.1674-7259.2006.08.007.
    [13]
    吴晓芳, 柏仲干, 代大海, 等. 对SAR的方位向间歇采样转发干扰[J]. 信号处理, 2010, 26(1): 1–6. doi: 10.3969/j.issn.1003-0530.2010.01.001.

    WU Xiaofang, BAI Zhonggan, DAI Dahai, et al. Azimuth intermittent sampling repeater jamming to SAR[J]. Signal Processing, 2010, 26(1): 1–6. doi: 10.3969/j.issn.1003-0530.2010.01.001.
    [14]
    胡东辉, 吴一戎. 合成孔径雷达散射波干扰研究[J]. 电子学报, 2002, 30(12): 1882–1884. doi: 10.3321/j.issn:0372-2112.2002.12.040.

    HU Donghui and WU Yirong. The scatter-wave jamming to SAR[J]. Acta Electronica Sinica, 2002, 30(12): 1882–1884. doi: 10.3321/j.issn:0372-2112.2002.12.040.
    [15]
    ITU. Radio regulations[EB/OL]. https://www.itu.int/en/publications/ITU-R/Pages/publications.aspx?parent=R-REG-RR-2020&media=electronic, 2020.
    [16]
    TAO Mingliang, SU Jia, HUANG Yan, et al. Mitigation of radio frequency interference in synthetic aperture radar data: Current status and future trends[J]. Remote Sensing, 2019, 11(20): 2438. doi: 10.3390/rs11202438.
    [17]
    YANG Huizhang, TAO Mingliang, CHEN Shengyao, et al. On the mutual interference between spaceborne SARs: Modeling, characterization, and mitigation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(10): 8470–8485. doi: 10.1109/TGRS.2020.3036635.
    [18]
    YANG Huizhang, LI Kun, LI Jie, et al. BSF: Block subspace filter for removing narrowband and wideband radio interference artifacts in single-look complex SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5211916. doi: 10.1109/TGRS.2021.3096538.
    [19]
    LI Ning, LV Zongsen, and GUO Zhengwei. Observation and mitigation of mutual RFI between SAR satellites: A case study between Chinese GaoFen-3 and European sentinel-1A[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5112819. doi: 10.1109/TGRS.2022.3170363.
    [20]
    FERRELL B H. Interference suppression in UHF synthetic aperture radar[C]. SPIE 2487, Algorithms for Synthetic Aperture Radar Imagery II, Orlando, USA, 1995: 96–106. doi: 10.1117/12.210830.
    [21]
    LORD R T and INGGS M R. Approaches to RF interference suppression for VHF/UHF synthetic aperture radar[C]. 1998 South African Symposium on Communications and Signal Processing, Rondebosch, South Africa, 1998: 95–100. doi: 10.1109/COMSIG.1998.736929.
    [22]
    POTSIS A, REIGBER A, and PAPATHANASSIOU K P. A phase preserving method for RF interference suppression in P-band synthetic aperture radar interferometric data[C]. IEEE 1999 International Geoscience and Remote Sensing Symposium, Hamburg, Germany, 1999: 2655–2657. doi: 10.1109/IGARSS.1999.771607.
    [23]
    王彦平, 彭海良, 吴一戎, 等. 合成孔径雷达射频干扰抑制的现状及展望[J]. 航天电子对抗, 2002(6): 18–24. doi: 10.3969/j.issn.1673-2421.2002.06.005.

    WANG Yan, PENG Hailiang, WU Yirong, et al. Current status and prospect of RFI suppression for SAR[J]. Aerospace Electronic Warfare, 2002(6): 18–24. doi: 10.3969/j.issn.1673-2421.2002.06.005.
    [24]
    ZHANG Shuangxi, XING Mengdao, GUO Rui, et al. Interference suppression algorithm for SAR based on time-frequency transform[J]. IEEE transactions on Geoscience and Remote Sensing, 2011, 49(10): 3765–3779. doi: 10.1109/TGRS.2011.2164409.
    [25]
    LI Dong, LIU Hongqing, and YANG Lisheng. Efficient time-varying interference suppression method for synthetic aperture radar imaging based on time-frequency reconstruction and mask technique[J]. IET Radar, Sonar & Navigation, 2015, 9(7): 827–834. doi: 10.1049/iet-rsn.2014.0218.
    [26]
    ZHOU Feng, WU Renbiao, XING Mengdao, et al. Eigensubspace-based filtering with application in narrow-band interference suppression for SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(1): 75–79. doi: 10.1109/LGRS.2006.887033.
    [27]
    NGUYEN L H and TRAN T D. Robust and adaptive extraction of RFI signals from ultra-wideband radar data[C]. 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 2012: 7137–7140. doi: 10.1109/IGARSS.2012.6352017.
    [28]
    HUANG Yan, LIAO Guisheng, ZHANG Lei, et al. Efficient narrowband RFI mitigation algorithms for SAR systems with reweighted tensor structures[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 9396–9409. doi: 10.1109/TGRS.2019.2926440.
    [29]
    黄岩, 赵博, 陶明亮, 等. 合成孔径雷达抗干扰技术综述[J]. 雷达学报, 2020, 9(1): 86–106. doi: 10.12000/JR19113.

    HUANG Yan, ZHAO Bo, TAO Mingliang, et al. Review of synthetic aperture radar interference suppression[J]. Journal of Radars, 2020, 9(1): 86–106. doi: 10.12000/JR19113.
    [30]
    YANG Huizhang, CHEN Chengzhi, CHEN Shengyao, et al. A dictionary-based SAR RFI suppression method via robust PCA and chirp scaling algorithm[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(7): 1229–1233. doi: 10.1109/LGRS.2020.2997947.
    [31]
    YANG Huizhang, HE Yaomin, DU Yanlei, et al. Two-dimensional spectral analysis filter for removal of LFM radar interference in spaceborne SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5219016. doi: 10.1109/TGRS.2021.3132495.
    [32]
    YANG Huizhang, LANG Ping, LU Xingyu, et al. Robust block subspace filtering for efficient removal of radio interference in synthetic aperture radar images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5206812. doi: 10.1109/TGRS.2024.3369021.
    [33]
    FAN Weiwei, ZHOU Feng, TAO Mingliang, et al. Interference mitigation for synthetic aperture radar based on deep residual network[J]. Remote Sensing, 2019, 11(14): 1654. doi: 10.3390/rs11141654.
    [34]
    WEI Shunjun, ZHANG Hao, ZENG Xiangfeng, et al. CARNet: An effective method for SAR image interference suppression[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 114: 103019. doi: 10.1016/j.jag.2022.103019.
    [35]
    聂林, 韦顺军, 李佳慧, 等. 基于区域特征细化感知学习的星载SAR图像有源压制干扰抑制方法[J]. 雷达学报(中英文), 2024, 13(5): 985–1003. doi: 10.12000/JR24072.

    NIE Lin, WEI Shunjun, LI Jiahui, et al. Active blanket jamming suppression method for spaceborne SAR images based on regional feature refinement perceptual learning[J]. Journal of Radars, 2024, 13(5): 985–1003. doi: 10.12000/JR24072.
    [36]
    马晓岩, 秦江敏, 贺照辉, 等. 抑制SAR压制性干扰的三通道对消方法[J]. 电子学报, 2007, 35(6): 1015–1020. doi: 10.3321/j.issn:0372-2112.2007.06.002.

    MA Xiaoyan, QIN Jiangmin, HE Zhaohui, et al. Three-channel cancellation of SAR blanketing jamming suppression[J]. Acta Electronica Sinica, 2007, 35(6): 1015–1020. doi: 10.3321/j.issn:0372-2112.2007.06.002.
    [37]
    李京生, 孙进平, 毛士艺. 一种基于STAP的多通道SAR噪声干扰抑制方法[J]. 电光与控制, 2008, 15(12): 18–20, 67. doi: 10.3969/j.issn.1671-637X.2008.12.005.

    LI Jingsheng, SUN Jinping, and MAO Shiyi. A noise jamming suppression approach for multi-channel SAR based on STAP[J]. Electronics Optics & Control, 2008, 15(12): 18–20, 67. doi: 10.3969/j.issn.1671-637X.2008.12.005.
    [38]
    孟智超, 卢景月, 张磊. 前视多通道SAR自适应鉴别抑制欺骗干扰[J]. 雷达学报, 2019, 8(1): 82–89. doi: 10.12000/JR18081.

    MENG Zhichao, LU Jingyue, and ZHANG Lei. Forward-looking multi-channel SAR adaptive identification to suppress deception jamming[J]. Journal of Radars, 2019, 8(1): 82–89. doi: 10.12000/JR18081.
    [39]
    张双喜. 高分辨宽测绘带多通道SAR和动目标成像理论与方法[D]. [博士论文], 西安电子科技大学, 2014.

    ZHANG Shuangxi. High-resolution and wide-swath multi-channel SAR and moving target imaging theory and methods[D]. [Ph.D. dissertation], Xidian University, 2014.
    [40]
    BOLLIAN T, OSMANOGLU B, RINCON R, et al. Adaptive antenna pattern notching of interference in synthetic aperture radar data using digital beamforming[J]. Remote Sensing, 2019, 11(11): 1346. doi: 10.3390/rs11111346.
    [41]
    YANG Lin, ZHENG Huifang, FENG Jin, et al. Detection and suppression of narrow band RFI for synthetic aperture radar imaging[J]. Chinese Journal of Aeronautics, 2015, 28(4): 1189–1198. doi: 10.1016/j.cja.2015.06.018.
    [42]
    NATSUAKI R, MOTOHKA T, WATANABE M, et al. An autocorrelation-based radio frequency interference detection and removal method in azimuth-frequency domain for SAR image[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(12): 5736–5751. doi: 10.1109/JSTARS.2017.2775205.
    [43]
    MONTI-GUARNIERI A, GIUDICI D, and RECCHIA A. Identification of C-band radio frequency interferences from sentinel-1 data[J]. Remote Sensing, 2017, 9(11): 1183. doi: 10.3390/rs9111183.
    [44]
    BOLLIAN T, OSMANOGLU B, RINCON R F, et al. Detection and geolocation of P-band radio frequency interference using EcoSAR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(10): 3608–3616. doi: 10.1109/JSTARS.2018.2830745.
    [45]
    NATSUAKI R, JÄGER M, and PRATS-IRAOLA P. Similarity approach for radio frequency interference detection and correction in multi-receiver SAR[C]. 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, USA, 2020: 3770–3773. doi: 10.1109/IGARSS39084.2020.9323270.
    [46]
    LV Zongsen, ZHANG Hengrui, LI Ning, et al. A two-step approach for pulse RFI detection in SAR data[C]. 2021 IEEE Sensors, Sydney, Australia, 2021: 1–4. doi: 10.1109/SENSORS47087.2021.9639826.
    [47]
    LENG Xiangguang, JI Kefeng, and KUANG Gangyao. Radio frequency interference detection and localization in sentinel-1 images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11): 9270–9281. doi: 10.1109/TGRS.2021.3049472.
    [48]
    LI Ning, LV Zongsen, and GUO Zhengwei. Pulse RFI mitigation in synthetic aperture radar data via a three-step approach: Location, notch, and recovery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5225617. doi: 10.1109/TGRS.2022.3161368.
    [49]
    HUANG Bo, FATTAHI H, GHAEMI H, et al. Radio frequency interference detection and mitigation of NISAR data using slow time eigenvalue decomposition[C]. 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023: 5479–5482. doi: 10.1109/IGARSS52108.2023.10282324.
    [50]
    YANG Huizhang, LANG Ping, HE Yaomin, et al. Lambda-1 detector: Adaptive interference detection in synthetic aperture radar images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5202613. doi: 10.1109/TGRS.2025.3529623.
    [51]
    YU Junfei, LI Jingwen, SUN Bing, et al. Multiclass radio frequency interference detection and suppression for SAR based on the single shot multibox detector[J]. Sensors, 2018, 18(11): 4034. doi: 10.3390/s18114034.
    [52]
    ARTIEMJEW P, CHOJKA A, and RAPINSKI J. Deep learning for RFI artifact recognition in sentinel-1 data[J]. Remote Sensing, 2021, 13(1): 7. doi: 10.3390/rs13010007.
    [53]
    TAO Mingliang, TANG Shuting, LI Jieshuang, et al. Radio frequency interference detection for SAR data using spectrogram-based semantic network[C]. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021: 1662–1665. doi: 10.1109/IGARSS47720.2021.9553478.
    [54]
    LI Jieshuang, TAO Mingliang, ZHANG Xiang, et al. A semantic cognition enhancment network for interference detection in sentinel-1 SAR image[C]. 2021 CIE International Conference on Radar, Haikou, China, 2021: 1923–1926. doi: 10.1109/Radar53847.2021.10027879.
    [55]
    LU Xingyu, WANG Chenchen, XU Xiaofeng, et al. Automatic RFI identification for sentinel-1 based on siamese-type deep CNN using repeat-pass images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5231616. doi: 10.1109/TGRS.2022.3190488.
    [56]
    TAO Mingliang, LI Jieshuang, CHEN Junli, et al. Radio frequency interference signature detection in radar remote sensing image using semantic cognition enhancement network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5231714. doi: 10.1109/TGRS.2022.3190288.
    [57]
    CEN Xi, LI Yachao, HAN Zhaoyun, et al. Self-supervised learning method for SAR multiinterference suppression[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5220017. doi: 10.1109/TGRS.2023.3328019.
    [58]
    ZHAO Jiayi, WANG Yongliang, LIAO Guisheng, et al. Intelligent detection and segmentation of space-borne SAR radio frequency interference[J]. Remote Sensing, 2023, 15(23): 5462. doi: 10.3390/rs15235462.
    [59]
    SØRENSEN K A, HEISELBERG P, KUSK A, et al. Radio frequency interference in synthetic aperture radar images[C]. 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023: 2145–2148. doi: 10.1109/IGARSS52108.2023.10282756.
    [60]
    CHOJKA A, ARTIEMJEW P, and RAPIŃSKI J. RFI artefacts detection in sentinel-1 level-1 SLC data based on image processing techniques[J]. Sensors, 2020, 20(10): 2919. doi: 10.3390/s20102919.
    [61]
    DAN H. X marks the spot: Identifying MIM-104 patriot batteries from sentinel-1 SAR multi-temporal imagery[EB/OL]. https://medium.com/@HarelDan/x-marks-the-spot-579cdb1f534b. 2020.06.06
    [62]
    LI Ning, ZHANG Hengrui, LV Zongsen, et al. Simultaneous screening and detection of RFI from massive SAR images: A case study on European sentinel-1[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5231917. doi: 10.1109/TGRS.2022.3191815.
    [63]
    TAO Mingliang, LAI Siqi, LI Jieshuang, et al. Extraction and mitigation of radio frequency interference artifacts based on time-series sentinel-1 SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5217211. doi: 10.1109/TGRS.2021.3126485.
    [64]
    陶臣嵩, 陈思伟, 肖顺平. 基于深度学习模型的SAR图像间歇采样转发干扰检测[J]. 系统工程与电子技术, 2023, 45(11): 3465–3473. doi: 10.12305/j.issn.1001-506X.2023.11.12.

    TAO Chensong, CHEN Siwei, and XIAO Shunping. SAR image interrupted sampling repeater jamming detection based on deep learning models[J]. Systems Engineering and Electronics, 2023, 45(11): 3465–3473. doi: 10.12305/j.issn.1001-506X.2023.11.12.
    [65]
    张皓宇, 全斯农, 田元荣, 等. 多阶段联合的SAR图像灵巧压制干扰检测方法[J]. 雷达科学与技术, 2024, 22(4): 377–384, 390. doi: 10.3969/j.issn.1672-2337.2024.04.003.

    ZHANG Haoyu, QUAN Sinong, TIAN Yuanrong, et al. A detection method for multi-stage joint SAR images dexterous suppression jamming[J]. Radar Science and Technology, 2024, 22(4): 377–384, 390. doi: 10.3969/j.issn.1672-2337.2024.04.003.
    [66]
    CHAN H L and YEO T S. Noniterative quality phase-gradient autofocus (QPGA) algorithm for spotlight SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(5): 1531–1539. doi: 10.1109/36.718857.
    [67]
    SHANNON C E. A mathematical theory of communication[J]. The Bell System Technical Journal, 1948, 27(3): 379–423. doi: 10.1002/j.1538-7305.1948.tb01338.x.
    [68]
    BOCKER R P, HENDERSON T B, JONES S A, et al. New inverse synthetic aperture radar algorithm for translational motion compensation[C]. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, San Diego, USA, 1991: 298–310. doi: 10.1117/12.48388.
    [69]
    CHEN Jianlai, XING Mengdao, YU Hanwen, et al. Motion compensation/autofocus in airborne synthetic aperture radar: A review[J]. IEEE Geoscience and Remote Sensing Magazine, 2022, 10(1): 185–206. doi: 10.1109/MGRS.2021.3113982.
    [70]
    杨健, 殷君君. 极化雷达理论与遥感应用[M]. 北京: 科学出版社, 2020: 9–10.

    YANG Jian and YIN Junjun. Theory and Remote Sensing Application of Polarimetric Radar[M]. Beijing: Science Press, 2020: 9–10.
    [71]
    仇晓兰, 焦泽坤, 彭凌霄, 等. SARMV3D-1.0: SAR微波视觉三维成像数据集[J]. 雷达学报, 2021, 10(4): 485–498. doi: 10.12000/JR21112.

    QIU Xiaolan, JIAO Zekun, PENG Lingxiao, et al. SARMV3D-1.0: Synthetic aperture radar microwave vision 3D imaging dataset[J]. Journal of Radars, 2021, 10(4): 485–498. doi: 10.12000/JR21112.
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