利用多时相双极化ALOS-2/PALSAR-2影像的2023年积石山Ms 6.2地震建筑物损毁检测

赵金奇, 陈樟杰, 牛玉芬, 张双成, 闫鹏飞, 王霞迎

赵金奇, 陈樟杰, 牛玉芬, 张双成, 闫鹏飞, 王霞迎. 利用多时相双极化ALOS-2/PALSAR-2影像的2023年积石山Ms 6.2地震建筑物损毁检测[J]. 武汉大学学报 ( 信息科学版), 2025, 50(2): 284-296. DOI: 10.13203/j.whugis20240118
引用本文: 赵金奇, 陈樟杰, 牛玉芬, 张双成, 闫鹏飞, 王霞迎. 利用多时相双极化ALOS-2/PALSAR-2影像的2023年积石山Ms 6.2地震建筑物损毁检测[J]. 武汉大学学报 ( 信息科学版), 2025, 50(2): 284-296. DOI: 10.13203/j.whugis20240118
ZHAO Jinqi, CHEN Zhangjie, NIU Yufen, ZHANG Shuangcheng, YAN Pengfei, WANG Xiaying. Building Damages Detection of the 2023 Ms 6.2 Jishishan (Gansu, China) Earthquake Using Multi-temporal Dual Polarization ALOS-2 /PALSAR-2 Data[J]. Geomatics and Information Science of Wuhan University, 2025, 50(2): 284-296. DOI: 10.13203/j.whugis20240118
Citation: ZHAO Jinqi, CHEN Zhangjie, NIU Yufen, ZHANG Shuangcheng, YAN Pengfei, WANG Xiaying. Building Damages Detection of the 2023 Ms 6.2 Jishishan (Gansu, China) Earthquake Using Multi-temporal Dual Polarization ALOS-2 /PALSAR-2 Data[J]. Geomatics and Information Science of Wuhan University, 2025, 50(2): 284-296. DOI: 10.13203/j.whugis20240118

利用多时相双极化ALOS-2/PALSAR-2影像的2023年积石山Ms 6.2地震建筑物损毁检测

基金项目: 

国家自然科学基金 42307255

国家自然科学基金 41901286

河北省自然科学基金 D2023402033

河北省教育厅科学研究项目 QN2024231

江苏省“双创博士” JSSCBS20221531

江苏省科技副总项目 FZ20240048

详细信息
    作者简介:

    赵金奇,博士,副教授,主要研究方向包括极化SAR智能解译、湿地动态变化监测和矿山开采监测。masurq@cumt.edu.cn

    通讯作者:

    牛玉芬,博士,讲师。niuyufen@hebeu.edu.cn

Building Damages Detection of the 2023 Ms 6.2 Jishishan (Gansu, China) Earthquake Using Multi-temporal Dual Polarization ALOS-2 /PALSAR-2 Data

  • 摘要:

    2023-12-18凌晨,甘肃省积石山县发生Ms 6.2地震,造成17万间房屋严重损坏及倒塌。对震后建筑物损毁进行高精度检测,有助于快速了解建筑物损毁分布,为灾后应急救援提供重要支撑。合成孔径雷达(synthetic aperture radar,SAR)具备全天时、全天候对灾区进行观测的能力,且能够提供相位、强度与极化信息,如何充分利用SAR数据信息是精确提取损毁建筑物的关键。充分利用双极化ALOS-2/PALSAR-2影像强度与相位信息,联合双极化强度变化检测、相干性检测和合成孔径雷达干涉测量技术,对积石山地震进行建筑物损毁检测和同震形变场提取,并利用北京三号高分光学影像对建筑物损毁检测结果进行验证。实验结果表明:(1)双极化信息能够显著提升变化检测及同震形变场的监测精度;(2)经震前、震后高分辨率光学影像验证,证明联合强度变化检测和相干性检测技术均能够提取震区损毁建筑物,两者相互补充、互为印证;(3)同震形变场最大区域刘集乡、石塬镇,形变场周边地区大河家镇、官亭镇、吹麻滩镇均存在大量建筑损毁。

    Abstract:
    Objectives 

    On December 18, 2023, an Ms 6.2 earthquake occurred in Jishishan, Gansu Pro-vince, China, damaging 170 000 houses and severely affecting people's lives and property safety. Efficient and accurate detection can help to understand the distribution of building damage and provide crucial support for post-disaster emergency relief.

    Methods 

    This paper combines intensity change detection, cohe-rence detection, and interferometric synthetic aperture radar technology using intensity and phase information from multi-temporal dual-polarized ALOS-2/PALSAR-2 images to identify building damage regions and coseismic deformation fields. First, a likelihood ratio change detection method is used to detect change regions from multi-temporal dual-polarization intensity images. Moreover, optimal coherence interferometric phase is extracted using different polarization mode combinations to obtain a more accurate coseismic deformation field. In addition, coherence change detection is introduced to detect different degrees of surface change distribution using pre- and post-earthquake interferometric coherence based on dual-polarization information. Finally, we combine intensity and coherence change detection results with world settlement footprint 2019 to map building damage regions.

    Results 

    The Beijing⁃3 optical images with a 0.3 m resolution are used to verify the effective of proposed method in building damage mapping. And the experimental results show that: (1) Intensity change detection results based on dual-polarization information are significantly better than single polarization, and the coherence estimation results based on dual-polarization information are also superior to single polarization interferometric pairs. Besides, coseismic deformation field using dual-polarization information avoids more phase unwrapping errors, and the deformation magnitude is similar to the Sentinel-1 results. (2) In the seven earthquake-affected towns, using the intensity change detection with dual-polarization information can detect the building damage area about 0.448 9 km2 and using cohe-rence change detection can detect the building damage area about 1.004 7 km2. (3) Both temporal intensity and coherence change detection methods can extract building damage. In most case, damage regions with coherence-based change detection generally larger than intensity-based.

    Conclusions 

    (1) Dual-polarization information can significantly improve the accuracy of change detection and the coseismic deformation field.(2) Both intensity-based and coherence-based change detection using dual-polarization information can detect damaged buildings in the disaster area. (3) Compared to intensity-based change detection, coherence-based detection is more sensitive and detects a larger damage regions. (4) Intensity-based and coherence-based techniques can complement and corroborate each other, and effectively extract damaged buildings in the earthquake area.

  • 本文ALOS⁃2/PALSAR⁃2影像与AW3D 30 DEM数据由日本宇宙航空研究开发机构提供;Sentinel⁃2 光学影像数据由欧洲空间局提供;WSF 2019居民地数据由德国航空航天中心的地球观测中心提供;外业调绘数据由兰州理工大学提供,在此深表感谢!
    http://ch.whu.edu.cn/cn/article/doi/10.13203/j.whugis20240118
  • 图  1   积石山震区概况:

    Figure  1.   Overview Map of the Study Area

    图  2   技术流程图

    Figure  2.   Technical Flowchart

    图  3   不同极化建筑物损毁检测对比

    Figure  3.   Comparison of Building Damage Detection with Different Polarizations

    图  4   大河家镇3个区域光学影像的不同极化方式房屋损毁检测验证

    Figure  4.   Verification of Building Damage Detection Using Different Polarization Modes in Dahejia Town

    图  5   2022⁃02⁃24-2023⁃12⁃28同震干涉对不同极化相干性对比分析

    Figure  5.   Comparison of Coherences of Coseismic (2022-02-24-2023-12-28) Interferogram for Different Polarizations

    图  6   InSAR同震形变场(2022⁃02⁃24—2023⁃12⁃28)

    Figure  6.   InSAR Coseismic Deformation Field(2022-02-24—2023-12-28)

    图  7   基于双极化SAR影像的震区建筑物损毁检测结果

    Figure  7.   Building Damage Detection Results Based on Dual-Polarization SAR Images

    图  8   官亭镇利用光学影像房屋倒塌区验证结果

    Figure  8.   Verification Results of Building Damage in Guanting Town

    图  9   大河家镇利用光学影像房屋倒塌区验证结果

    Figure  9.   Verification Results of Building Damage in Dahejia Town

    表  1   研究数据种类和来源

    Table  1   Types and Sources of the Study Data

    数据类型数据来源观测日期分辨率/m
    SARALOS⁃2/PALSAR⁃2震前2019⁃02⁃2810
    2020⁃02⁃27
    2022⁃02⁃24
    震后2023⁃12⁃20
    光学北京三号震前2023⁃04⁃110.3
    震后2023⁃12⁃20
    Sentinel⁃2震前2023⁃12⁃1810
    DEMAW3D30震前2016—2011年30
    居民地数据WSF 2019震前2019年10
    下载: 导出CSV

    表  2   不同极化方式强度变化检测建筑物损毁面积统计/km2

    Table  2   Statistics of Building Damage Area Detected by Intensity Changes in Different Polarization Modes/km2

    数据类型官亭镇中川乡石塬乡吹麻滩镇柳沟乡刘集乡大河家镇合计
    HH0.011 60.001 90.000 60.010 300.001 00.013 40.038 8
    HV0.000 400000.000 40.000 20.001 0
    HH+HV0.065 00.031 10.014 40.196 70.007 60.020 80.113 40.448 9
    下载: 导出CSV

    表  3   双极化相干性检测及强度变化检测建筑物损毁面积统计/km2

    Table  3   Statistics of Building Damage Area Using Dual⁃Polarization Coherence and Intensity Change Detection/km2

    检测方法官亭镇中川乡石塬乡吹麻滩镇柳沟乡刘集乡大河家镇合计
    相干性差值[-0.8,-0.6)0.009 700.001 00.000 00.003 10.000 00.001 20.003 10.018 1
    [-0.6,-0.4)0.038 600.012 00.007 40.021 00.000 40.015 30.060 20.155 0
    [-0.4,-0.2]0.017 920.070 90.046 40.146 00.012 60.086 00.290 50.831 6
    强度变化检测0.065 000.031 10.014 40.196 70.007 60.020 80.113 40.448 9
    下载: 导出CSV
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  • 收稿日期:  2024-03-31
  • 刊出日期:  2025-02-04

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