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遥感技术与应用  2022, Vol. 37 Issue (2): 306-318    DOI: 10.11873/j.issn.1004-0323.2022.2.0306
综述     
高分辨率光学遥感影像变化检测算法在地震灾情调查中的应用
张飞舟1(),刘华亮1,张立福2(),岑奕2,孙雪剑2,张红明2
1.北京大学 遥感与地理信息系统研究所,北京 100871
2.中国科学院空天信息创新研究院 遥感科学国家重点实验室,北京 100101
Review of Change Detection Algorithm Using Optical Remote Sensing Images in Post-earthquake Damage Investigation
Feizhou Zhang1(),Hualiang Liu1,Lifu Zhang2(),Yi Cen2,Xuejian Sun2,Hongming Zhang2
1.Institute of Remote Sensing and Geographic Information Systems,School of Earth and Space Sciences,Peking University,Beijing 100871,China
2.State Key Laboratory of Remote Sensing Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China
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摘要:

遥感卫星可快速、动态地获取地震灾区大范围的高分辨率影像,已成为快速获取震后灾情信息的主要技术手段之一。基于震后灾情调查中广泛使用的光学遥感数据和变化检测算法,首先对遥感数据及其产品进行了归纳总结,在此基础上综述了基于高分辨率遥感影像的变化检测算法在震害提取中的应用,阐述了基于像元和面向对象两类变化检测方法的基本原理和优缺点,讨论和总结了应用中存在的问题和不足,以期为未来地震应急中的灾情调查工作提供参考。

关键词: 地震应急卫星遥感光学影像变化检测灾情信息提取    
Abstract:

Remote sensing satellites can quickly and dynamically acquire high-resolution images of large disaster area, and thus has become one of the main technical methods for post-earthquake damage investigation. This paper focuses on the optical remote sensing data and change detection algorithms widely used in post-earthquake disaster surveys. The satellite remote sensing data and products are summarized, and then the application of change detection algorithms using both pre- and post-earthquake images for damage investigation is reviewed. The basic principles, advantages and disadvantages of pixel-based and object-oriented change detection methods are described. The existing research results are classified and reviewed, and the problems and deficiencies in practical applications are discussed and summarized, with a view to providing references and benefits for future post-earthquake damage investigation work.

Key words: Earthquake emergency    Satellite remote sensing    Optical image    Change detection    Post-earthquake damage investigation
收稿日期: 2020-11-15 出版日期: 2022-06-17
ZTFLH:  P314.73  
基金资助: 国家重点研发计划项目(2017YFC1500900);国家自然科学基金重点项目(41830108)
通讯作者: 张立福     E-mail: zhangfz@pku.edu.cn;zhanglf@radi.ac.cn
作者简介: 张飞舟(1966-),男,湖南邵阳人,博士,教授,主要从事卫星导航、智慧城市研究。E?mail:zhangfz@pku.edu.cn
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引用本文:

张飞舟,刘华亮,张立福,岑奕,孙雪剑,张红明. 高分辨率光学遥感影像变化检测算法在地震灾情调查中的应用[J]. 遥感技术与应用, 2022, 37(2): 306-318.

Feizhou Zhang,Hualiang Liu,Lifu Zhang,Yi Cen,Xuejian Sun,Hongming Zhang. Review of Change Detection Algorithm Using Optical Remote Sensing Images in Post-earthquake Damage Investigation. Remote Sensing Technology and Application, 2022, 37(2): 306-318.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.2.0306        http://www.rsta.ac.cn/CN/Y2022/V37/I2/306

卫星传感器全色分辨率/m多光谱分辨率/m重访周期/d轨道高度/km幅宽/km发射时间所属国
WorldView-4P/M0.311.241.1617162016.11美国
WorldView-3P/M0.311.241.1617132014.08美国
GeoEye-1P/M0.411.652.6681152008.09美国
WorldView-2P/M0.461.851.1770162009.01美国
WorldView-1P0.5-1.7496182007.09美国
Pléiades-1P/M0.521695202011.12法国
高景一号P/M0.521530122016.12中国
Kompsat-3AP/M0.552.20.9528132015.03韩国
QuickBirdP/M0.612.441.5~3450172001.01美国
Kompsat-3P/M0.72.81.5685162012.05韩国
Resurs-PP/M0.733475382013.06俄罗斯
EROS-BP0.7-352072006.04以色列
北京二号P/M0.83.21651242015.07中国
IKONOSP/M0.823.21.5~3681111999.09美国
Kompsat-2P/M142~3685152007.07韩国
高分二号P/M145631452014.08中国
SPOT-6/7P/M1.561~5694602012.09法国
高分一号P/M284645602013.04中国
福卫二号P/M280.5891242004.05中国
资源三号P/M2.165506512012.01中国
资源一号P/M2.36103780602011.12中国
SPOT-5P/M2.5102~3822602002.05法国
ALOSP/A2.5102692352006.01日本
北京一号P/M4243~5686242005.01中国
RapidEyeM-51630772008.08德国
SPOT-4P/M10202~3831601998.03法国
Landsat 7ETM+1530167051851999.04美国
Landsat 8OLI1530167051852013.02美国
表1  地震灾情调查中的高分辨率遥感卫星
服务商旗下的遥感卫星(部分已退役)影像产品
产品名称产品等级处理程度发布时长
DigitalGlobe

IKONOS

QuickBird

GeoEye-1

Worldview-1–4

基础产品1B仅对原始数据进行了辐射和传感器粗校正24 h
标准产品OR2A进一步进行了几何校正和地图投影24 h
2A在OR2A产品的基础上进行了地形粗校正48 h
正射产品3D利用精细的DEM数据进行了正射校正N/A
Astrium

Pléiades-1A/1B

Vision-1

SPOT-6/7

初级产品1仅对原始数据进行了几何校正和辐射校正12 h
标准正射产品2进一步进行了正射校正和地图投影等12 h
定制化产品3根据用户需求提供的定制化正射产品N/A
21at

北京一号

北京二号

辐射校正产品1对原始数据进行了辐射和传感器校正N/A
几何校正产品2进一步进行了系统几何校正N/A
正射校正产品4利用DEM进行了粗正射校正N/A
表2  主要的高分辨率卫星数据供应商的影像产品
方法方法细分优点缺点代表性应用
基于像元的方法直接比较法代数法简单、易操作,便于解释对影像的预处理的要求高;存在“椒盐效应”;只能判断是否发生变化

Yusuf等[38]

Zhao等[34]

回归法稳定可靠需要多个时相的震前影像,需要分别配准,通常难以实现Kohiyama等[22]
相关系数法可以消除地震前后影像的灰度和对比度的差异;可用于配准需确定窗口大小和阈值;配准误差会降低相关性

Rathje等[44]

Chesnel等[26]

特征提取法植被指数法凸显植被信息,在检测滑坡时非常有效只能检测与植被相关的变化

Chini等[41]

Yang等[33]

主成分变换法利用了多波段信息,可消除冗余和相关性物理意义不明确,震害提取需要一定的经验和分析

Tomowski等[31]

Gong等[51]

分类后比较法克服了影像因获取条件引起差异;可以提供变化的类型需要训练样本;需要多次分类;分类误差会传递Xu等[53]
面向对象的方法目视解译简单实用,精度和可靠性高需投入大量的人力资源

Yamazaki等[62]

Zhang等[43]

多时相叠加法对所有时相的影像只需进行一次分割;同一对象具有相同的几何特征需要配准以保证同一对象的对应;震后新出现的地物对象会被忽略

Gusella等[20]

Samadzadegan等[30]

直接比较法简单、直接;可以引入基于像元的比较方法需要精确地分割对象;需解决同一对象的对应问题

Tiede等[27]

Anniballe等[32]

表3  地震灾情调查中的变化检测算法
图1  各地震前后的光学卫星影像获取时间
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