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[1]DU Haoguo,ZHANG Fanghao,LU Yongkun,et al.Recognition of the Earthquake Damage to Buildings in the 2021 Yangbi,Yunnan MS6.4 Earthquake Area Based on Multi-source Remote Sensing Images[J].Journal of Seismological Research,2021,44(03):490-498.

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Recognition of the Earthquake Damage to Buildings in the 2021 Yangbi,Yunnan M_S6.4 Earthquake Area Based on Multi-source Remote Sensing Images

Journal of Seismological Research[ISSN 1000-0666/CN 53-1062/P] Volume: 44 Number of periods: 2021 03 Page number: 490-498 Column: Public date: 2021-10-20

Title:: Recognition of the Earthquake Damage to Buildings in the 2021 Yangbi,Yunnan M_S6.4 Earthquake Area Based on Multi-source Remote Sensing Images

Author(s):: DU Haoguo¹; ZHANG Fanghao¹; LU Yongkun¹; LIN Xuchuan²; DENG Shurong¹; CAO Yanbo¹; (1.Yunnan Earthquake Agency,Kunming 650224,Yunnan,China)(2.Institute of Engineering Mechanics,China Earthquake Administration,Harbin 150080,Heilongjiang,China)

Keywords:: the Yangbi M_S6.4 earthquake; identification of earthquake-damage to buildings; assessment on the disaster losses; digital surface model; high-resolution image

CLC:: P315.94

DOI:: -

Abstract:: Rapid identification of the earthquake damage to buildings in the earthquake-stricken area is of great significance for scientific and effective assessment of losses from earthquake disasters.Based on the high-resolution,remote-sensing images of post-earthquake field investigation obtained by UAV and digital surface model(DSM),we propose an identification method of earthquake damage to buildings based on multi-source,remote-sensing images.In the light of this method,we first do the multi-scale segmentation of the surface feature,then extract buildings’ information,and weed out other features.Further,we identify the damage,structures and the floors of the buildings according to the spectrum,texture and shape of the buildings on the images.We apply our method to the identification of the damage to the buildings in the Yangbi M_S6.4 earthquake on 21th,May 2021.The results show that,compared with the traditional manual investigation of the damage in the earthquake-affected areas,our method is more effective and more accurate.

References:: 陈晋,习聪望,陈文凯,等.2018.基于无人机、高分卫星遥感影像的甘肃省陇南市建筑物空间化研究[J].地震研究,41(2):192-200.

代沁伶,罗斌,郑晨,等.2020.区域多尺度马尔可夫随机场的遥感影像分类[J].遥感学报,24(3):245-253.

丁胜,袁修孝,陈黎.2010.粒子群优化算法用于高光谱遥感影像分类的自动波段选择[J].测绘学报,39(3):257-263.

杜浩国,陈亮,林旭川,等.2019.云南红河县城区房屋地震灾害三维仿真模拟方法[J].地震研究,42(2):218-229.

杜浩国,林旭川,张方浩,等.2021.基于蚁群优化算法的应急避难场所选址模型研究[J].自然灾害学报,30(2):89-101.

杜浩国,张方浩,邓树荣,等.2018.震后极灾区无人机最优航拍区域选择[J].地震研究,41(2):209-215.

杜妍开,龚丽霞,李强,等.2020.基于最优分割的高分辨率遥感影像震害建筑物识别技术[J].地震学报,42(6):760-768.

和仕芳,邓树荣,张方浩,等.2016.旋翼无人机在建筑物抗震能力调查中的应用[J].地震研究,39(4):673-679.

李强,张景发.2016.不同特征融合的震后损毁建筑物识别研究[J].地震研究,39(3):486-493.

卢永坤,周洋,代博洋,等.2019.2018年云南墨江5.9级地震房屋震害特征与烈度评定[J].地震研究,42(2):172-178.

马燕妮,明冬萍,杨海平.2017.面向对象影像多尺度分割最大异质性参数估计[J].遥感学报,21(4):566-578.

王昶,张永生,纪松,等.2021.建筑物变化的多特征融和及随机多图综合检测法[J].测绘学报,50(2):235-247.

曾涛.2010.汶川地震重灾区多源影像处理及震害信息提取方法研究[D].成都:成都理工大学.

张方浩,卢永坤,邓树荣,等.2020.云南地区区域地震灾害特征[J].地震研究,43(1):134-143.

张建国.2014.地震烈度的评定及其意义[J].中国减灾,(17):42-43.

张雪华,王晓青,杜晓霞,等.2019.基于无人机遥感影像及其点云特征的建筑物震害提取[J].地震研究,42(2):230-235.

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Last Update: 2021-10-20