WANG Wenxin, YANG Defang, LI Long, LI Wenjun, FENG Guangcai, HE Lijia, XIONG Zhiqiang, LI Ning, JIANG Hongbo, LUO Wulinhong, WANG Yilin. Image Initial Registration Algorithm for Lutan-1 Satellite Based on Scale-Invariant Feature Transform-Like Algorithm— A Case Study of the Jishishan Earthquake[J]. Geomatics and Information Science of Wuhan University, 2025, 50(2): 377-390. DOI: 10.13203/j.whugis20240087
Citation: WANG Wenxin, YANG Defang, LI Long, LI Wenjun, FENG Guangcai, HE Lijia, XIONG Zhiqiang, LI Ning, JIANG Hongbo, LUO Wulinhong, WANG Yilin. Image Initial Registration Algorithm for Lutan-1 Satellite Based on Scale-Invariant Feature Transform-Like Algorithm— A Case Study of the Jishishan Earthquake[J]. Geomatics and Information Science of Wuhan University, 2025, 50(2): 377-390. DOI: 10.13203/j.whugis20240087

Image Initial Registration Algorithm for Lutan-1 Satellite Based on Scale-Invariant Feature Transform-Like Algorithm— A Case Study of the Jishishan Earthquake

More Information
  • Received Date: March 13, 2024
  • Available Online: June 04, 2024
  • Objectives 

    The Lutan-1 (LT-1) synthetic aperture radar (SAR) satellite, the first group of L-band fully polarimetric civilian SAR satellites in China with interferometry as its core mission, is suitable for monitoring and emergency response to disasters such as earthquakes and landslides. However, due to the inaccuracy of the satellite's real-time orbit data, the initial registration precision of the LT-1 SAR images is not high, which easily leads to registration failure and affects the efficiency of the image-automated registration process.

    Methods 

    In response to this issue, this study proposes a LT-1 image initial registration method and processing strategy based on a scale invariant feature transform like (SIFT-Like) algorithm to enhance the registration success rate. Taking the Ms 6.2 earthquake in Jishishan, Gansu, on December 18, 2023, as an example, the algorithm’s reliability was verified, with the registration success rate increased from 34.5% to 100%, successfully obtaining the coseismic deformation field of this earthquake. Furthermore, the coseismic deformation results of Sentinel-1A/B for this earthquake were also acquired for precision validation and analysis of the LT-1 results.

    Results 

    Integration of LT-1 and Sentinel-1A/B results indicate that the earthquake was primarily characterized by uplift, with a maximum uplift of 6.3 cm, classifying it as a thrust earthquake. Using pre-earthquake Sentinel-1 ascending and descending orbit interferometric results, 195 and 179 landslides were respectively identified, and it was observed that the liquefaction landslide-debris flow triggered by the earthquake in Caotan Village had exhibited significant deformation before the event, with a deformation rate exceeding 9 mm/a. And the impact of irregular segmentation of LT-1 images on algorithm effectiveness is discussed, and the potential applications of LT-1 satellites in earthquake and geological hazard monitoring are highlighted.

    Conclusions 

    The proposed algorithm effectively eliminates the registration issue of the LT-1 satellite. With the increasing archive data of LT-1, this algorithm can better highlight its advantages. Combining the LT-1 data and the algorithm can better serve the deformation monitoring and emergency response of earthquakes and geological disasters in the future.

  • [1]
    李涛, 唐新明, 李世金, 等. L波段差分干涉SAR卫星基础形变产品分类[J]. 测绘学报, 2023, 52(5): 769-779.

    LI Tao, TANG Xinming, LI Shijin, et al. Classification of Basic Deformation Products of L-band Differential Interferometric SAR Satellite[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(5): 769-779.
    [2]
    LI T, TANG X M, ZHOU X Q, et al. Lutan-1 SAR Main Applications and Products[C]//The 14th European Conference on Synthetic Aperture Radar, Leipzig, Germany, 2022.
    [3]
    ZHANG X F, LI T, ZHANG X, et al. A Feasibility Study of LT-1 SAR Satellite for Permafrost Deformation Monitoring[C]//SAR in Big Data Era, Beijing, China, 2023.
    [4]
    JI Y N, ZHANG X, LI T, et al. Mining Deformation Monitoring Based on Lutan-1 Monostatic and Bistatic Data[J]. Remote Sensing, 2023, 15(24): 5668.
    [5]
    LI T, TANG X M, ZHOU X Q, et al. Deformation Products of Lutan-1(LT-1) SAR Satellite Constellation for Geohazard Monitoring[C]//IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022.
    [6]
    WANG Z D, LI T, TANG W, et al. Identification Capability Analysis of Landslide Hazards for LT-1 and Sentinel-1 Using Time Series SAR Interferometry: A Case Study of Maoxian, Sichuan[C]// SAR in Big Data Era, Beijing, China, 2023.
    [7]
    刘斌, 张丽, 葛大庆, 等. 陆地探测一号卫星滑坡大变形InSAR监测应用[J]. 武汉大学学报(信息科学版),2024, 49(10): 1753-1762.

    LIU Bin, ZHANG Li, GE Daqing, et al. Application of InSAR Monitoring Large Deformation of Landslides Using Lutan-1 Constellation[J]. Geomatics and Information Science of Wuhan University,2024, 49(10): 1753-1762.
    [8]
    LI H, LI B Q, LI Y S, et al. The Stability Analysis of Mt. Gongga Glaciers Affected by the 2022 Lu⁃ding Ms 6.8 Earthquake Based on Lutan-1 and Sentinel-1 Data[J]. Remote Sensing, 2023, 15(15): 3882.
    [9]
    李永生, 李强, 焦其松, 等. 陆探一号SAR卫星星座在地震行业的应用与展望[J]. 武汉大学学报(信息科学版),2024, 49(10): 1741-1752.

    LI Yongsheng, LI Qiang, JIAO Qisong, et al. Application and Prospect of Lutan-1 SAR Satellite Constellation in Earthquake Industry[J]. Geomatics and Information Science of Wuhan University,2024, 49(10): 1741-1752.
    [10]
    卢丽君. InSAR影像配准及其并行化算法研究[D]. 武汉: 武汉大学, 2005.

    LU Lijun. Research on InSAR Image Registration and Its Parallelization Algorithm[D]. Wuhan: Wuhan University, 2005.
    [11]
    赫永杰.基于多尺度SPOMF-FMI的重复轨道InSAR图像配准研究[D]. 北京: 北京交通大学, 2012.

    HE Yongjie. Research of Repeat Orbital InSAR Image Registration Based on Multi-scale SPOMF-FMI[D]. Beijing: Beijing Jiaotong University, 2012.
    [12]
    陈富龙, 王超, 张红, 等. 附带星历参数的星载合成孔径雷达影像自动配准算法[J]. 遥感学报, 2008, 12(4): 553-560.

    CHEN Fulong, WANG Chao, ZHANG Hong, et al. Automatic Registration of Spaceborne SAR Images Accompanied by Ephemeris[J]. Journal of Remote Sensing, 2008, 12(4): 553-560.
    [13]
    吴文豪, 李陶, 龙四春, 等. 实时轨道条件下Sentinel-1卫星影像干涉配准[J]. 武汉大学学报(信息科学版), 2019, 44(5): 745-750.

    WU Wenhao, LI Tao, LONG Sichun, et al. Core-gistration of Sentinel-1 TOPS Data for Interferometric Processing Using Real-Time Orbit[J]. Geomatics and Information Science of Wuhan University, 2019, 44(5): 745-750.
    [14]
    吴文豪, 张磊, 李陶, 等. 基于几何配准的多模式SAR影像配准及其误差分析[J]. 测绘学报, 2019, 48(11): 1439-1451.

    WU Wenhao, ZHANG Lei, LI Tao, et al. Coregistration Scheme and Error Analysis of Multi-mode SAR Image Based on Geometric Coregistration[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(11): 1439-1451.
    [15]
    GONG M G, ZHAO S M, JIAO L C, et al. A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information[J]. IEEE Transactions on Geoscience and Remote Sen⁃sing, 2014, 52(7): 4328-4338.
    [16]
    中国地震台网中心. 12月18日23时59分在甘肃临夏回族自治州积石山县发生6.2级地震[EB/OL]. (2023-12-19) [2024-02-28]. https://www.cenc.ac.cn/cenc/dzxx/409064/index.html.

    China Earthquake Networks Center. An Ms 6.2 Earthquake Struck Jishishan County, Linxia Hui Autonomous Prefecture, Gansu Province, at 23:59 on 18th December[EB/OL]. (2023-12-19) [2024-02-28]. https://www.cenc.ac.cn/cenc/dzxx/409064/index.html.
    [17]
    王立朝, 侯圣山, 董英, 等. 甘肃积石山Ms 6.2地震的同震地质灾害基本特征及风险防控建议[J]. 中国地质灾害与防治学报, 2024, 35(3): 108-118.

    WANG Lichao, HOU Shengshan, DONG Ying, et al. Basic Characteristics of Coseismic Geological Hazards Induced by the Ms 6.2 Jishishan Earthquake and Suggestions for Their Risk Control[J]. The Chinese Journal of Geological Hazard and Control, 2024, 35(3): 108-118.
    [18]
    王运生, 赵波, 吉锋, 等. 2023年甘肃积石山Ms 6.2地震震害异常的启示[J]. 成都理工大学学报(自然科学版), 2024, 51(1): 1-8.

    WANG Yunsheng, ZHAO Bo, JI Feng, et al. Preliminary Insights into the Hazards Triggered by the 2023 Ms 6.2 Jishishan Earthquake in Gansu Province[J]. Journal of Chengdu University of Technology (Science & Technology Edition), 2024, 51(1): 1-8.
    [19]
    LOWE D G. Object Recognition from Local Scale-Invariant Features[C]//The 7th IEEE International Conference on Computer Vision, Kerkyra, Greece, 1999.
    [20]
    LOWE D G. Distinctive Image Features from Scale-Invariant Keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
    [21]
    DELLINGER F, DELON J, GOUSSEAU Y, et al. SAR-SIFT: A SIFT-Like Algorithm for SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1): 453-466.
    [22]
    廖明生, 王腾. 时间序列InSAR技术与应用[M]. 北京: 科学出版社, 2014.

    LIAO Mingsheng, WANG Teng. Time Series InSAR Technology and Its Application[M]. Beijing: Science Press, 2014.
    [23]
    应急管理部国家自然灾害防治研究院. 甘肃临夏回族自治州积石山县6.2级地震发震构造环境分析[EB/OL]. (2023-12-20) [2024-02-28]. http://www.ninhm.ac.cn/content/details_104_4376.html.

    National Institute for Natural Disaster Prevention and Control of the Ministry of Emergency Management. Analysis of the Seismogenic Tectonic Environment of the 6.2 Magnitude Earthquake in Jishishan County, Linxia Hui Autonomous Prefecture, Gansu Province[EB/OL]. (2023-12-20)[2024-02-28]. http://www.ninhm.ac.cn/content/details_104_4376.html.
    [24]
    杨九元, 温扬茂, 许才军. InSAR观测揭示的2023年甘肃积石山Ms 6.2地震发震构造[J]. 武汉大学学报(信息科学版),2024,DOI: 10.13203/j.whugis20230501. doi: 10.13203/j.whugis20230501

    YANG Jiuyuan, WEN Yangmao, XU Caijun. Seismogenic Fault Structure of the 2023 Ms 6.2 Jishishan (Gansu,China) Earthquake Revealed by InSAR Observations[J]. Geomatics and Information Science of Wuhan University,2024,DOI: 10.13203/j.whugis20230501. doi: 10.13203/j.whugis20230501
    [25]
    袁道阳, 张培震, 雷中生, 等. 青海拉脊山断裂带新活动特征的初步研究[J]. 中国地震, 2005, 21(1): 93-102.

    YUAN Daoyang, ZHANG Peizhen, LEI Zhong-sheng, et al. A Preliminary Study on the New Activity Features of the Lajishan Mountain Fault Zone in Qinghai Province[J]. Earthquake Research in China, 2005, 21(1): 93-102.
    [26]
    张波. 西秦岭北缘断裂西段与拉脊山断裂新活动特征研究[D]. 兰州: 中国地震局兰州地震研究所, 2012.

    ZHANG Bo. Study on New Activity Characteristics of Western Segment of Fault and Lajishan Fault in the Northern Margin of Western Qinling Mountains[D]. Lanzhou: Lanzhou Institute of Seismology, China Earthquake Administration, 2012.
    [27]
    WERNER C, WEGMÜLLER U, STROZZI T, et al. GAMMA SAR and Interferometric Processing Software[C]//ERS-Envisat Symposium, Gothenburg, Sweden, 2000.
    [28]
    WEGNÜLLER U, WERNER C, STROZZI T, et al. Sentinel-1 Support in the GAMMA Software[J]. Procedia Computer Science, 2016, 100: 1305-1312.
    [29]
    BERARDINO P, FORNARO G, LANARI R, et al. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2375-2383.
    [30]
    LUO S R, FENG G C, XIONG Z Q, et al. An Improved Method for Automatic Identification and Assessment of Potential Geohazards Based on MT-InSAR Measurements[J]. Remote Sensing, 2021, 13(17): 3490.
    [31]
    陈博, 宋闯, 陈毅, 等. 2023年甘肃积石山Ms 6.2地震同震滑坡和建筑物损毁情况应急识别与影响因素研究[J]. 武汉大学学报(信息科学版), 2024, DOI: 10.13203/J.whugis20230497. doi: 10.13203/J.whugis20230497

    CHEN Bo, SONG Chuang, CHEN Yi, et al. Emergency Identification and Influencing Factor Analysis of Coseismic Landslides and Building Da-mages Induced by the 2023 Ms 6.2 Jishishan (Gansu, China) Earthquake[J]. Geomatics and Information Science of Wuhan University, 2024, DOI:10.13203/J.whugis20230497. doi: 10.13203/J.whugis20230497
    [32]
    李为乐, 许强, 李雨森, 等. 2023年积石山Ms 6.2地震同震地质灾害初步分析[J]. 成都理工大学学报(自然科学版), 2024, 51(1): 33-45.

    LI Weile, XU Qiang, LI Yusen, et al. Preliminary Analysis of the Coseismic Geohazards Induced by the 2023 Ms 6.2 Jishishan Earthquake[J]. Journal of Chengdu University of Technology (Science & Technology Edition), 2024, 51(1): 33-45.
    [33]
    许强, 彭大雷, 范宣梅, 等. 甘肃积石山Ms 6.2地震触发青海中川乡液化型滑坡-泥流特征与成因机理[J]. 武汉大学学报(信息科学版),2024,DOI: 10.13203/j.whugis20240007. doi: 10.13203/j.whugis20240007

    XU Qiang, PENG Dalei, FAN Xuanmei, et al. Preliminary Study on the Characteristics and Initiation Mechanism of Zhongchuan Town Flowslide Triggered by the Ms 6.2 Jishishan Earthquake in Gansu Province[J]. Geomatics and Information Science of Wuhan University, 2024, DOI: 10.13203/j.whugis20240007. doi: 10.13203/j.whugis20240007
    [34]
    李亚军, 岳东霞, 陈冠, 等. 积石山地震诱发金田-草滩村滑坡-泥流灾害链过程与成因[J]. 兰州大学学报(自然科学版), 2024, 60(1): 1-5.

    LI Yajun, YUE Dongxia, CHEN Guan, et al. A Preliminary Analysis of the Process and Cause of the Jintian-Caotan Landslide-Mudflow Hazard Chain Induced by the Jishishan Earthquake[J]. Journal of Lanzhou University (Natural Sciences), 2024, 60(1): 1-5.
    [35]
    WESSEL P, LUIS J F, UIEDA L, et al. The Generic Mapping Tools Version 6[J]. Geochemistry, Geophysics, Geosystems, 2019, 20(11): 5556-5564.
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