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Du Yankai, Gong Lixia, Li Qiang, Zhang Jingfa. 2020: Earthquake damage building identification technology based on high resolution remote sensing image with optimal segmentation. Acta Seismologica Sinica, 42(6): 760-768. DOI: 10.11939/jass.20200027
Citation: Du Yankai, Gong Lixia, Li Qiang, Zhang Jingfa. 2020: Earthquake damage building identification technology based on high resolution remote sensing image with optimal segmentation. Acta Seismologica Sinica, 42(6): 760-768. DOI: 10.11939/jass.20200027
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Earthquake damage building identification technology based on high resolution remote sensing image with optimal segmentation

  • National Institute of Natural Hazards，Ministry of Emergency Management of China，Beijing 100085，China

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  • Received Date: February 23, 2020
  • Revised Date: May 28, 2020
  • Available Online: February 06, 2021
  • Published Date: November 14, 2020
    Graphical Abstract
    • Abstract
  • In order to improve the efficiency and the accuracy of information extraction about earthquake damage building, based on high resolution remote sensing image after the earthquake, and according to the features of earthquake damage buildings in remote sensing images, we took a case study of MS7.0 Haiti earthquake in 2010, through the ESP algorithm automatically chose the optimal segmentation scale to multi-scale segmentation of images, used object-oriented method to Haiti high-resolution remote sensing image information extraction of earthquake damage buildings. At the same time, it is compared with Support Vector Machine based on pixel, BP neural network and Decision Tree classification method based on CART algorithm, the experimental results show that the object-oriented classification method has better visual effect and higher classification accuracy, which is beneficial to the accurate extraction and rapid evaluation of earthquake damage information after the earthquake.
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    • References (34)
  • 陈云浩,冯通,史培军,王今飞. 2006. 基于面向对象和规则的遥感影像分类研究[J]. 武汉大学学报(信息科学版),31(4):316–320.
    Chen Y H,Feng T,Shi P J,Wang J F. 2006. Classification of remote sensing image based on object oriented and class rules[J]. Geomatics and Information Science of Wuhan University,31(4):316–320 (in Chinese).
    龚丽霞,李强,张景发,曾琪明,刘明众,李成龙. 2013. 面向对象的房屋震害变化检测方法[J]. 地震,33(2):109–114. doi: 10.3969/j.issn.1000-3274.2013.02.014
    Gong L X,Li Q,Zhang J F,Zeng Q M,Liu M Z,Li C L. 2013. Object-oriented detection of earthquake building damages[J]. Earthquake,33(2):109–114 (in Chinese).
    李强,张景发,龚丽霞,薛腾飞,蒋洪波. 2018. SAR图像纹理特征相关变化检测的震害建筑物提取[J]. 遥感学报,22(增刊1):128–138.
    Li Q,Zhang J F,Gong L X,Xue T F,Jiang H B. 2018. Extraction of earthquake-collapsed buildings based on correlation change detection of multi-texture features in SAR images[J]. Journal of Remote Sensing,22(S1):128–138 (in Chinese).
    李强,张景发,罗毅,焦其松. 2019. 2017年“8.8”九寨沟地震滑坡自动识别与空间分布特征[J]. 遥感学报,23(4):785–795.
    Li Q,Zhang J F,Luo Y,Jiao Q S. 2019. Recognition of earthquake-induced landslide and spatial distribution patterns triggered by the Jiuzhaigou earthquake in August 8,2017[J]. Journal of Remote Sensing,23(4):785–795 (in Chinese).
    牟多铎,刘磊. 2019. ELM与SVM在高光谱遥感图像监督分类中的比较研究[J]. 遥感技术与应用,34(1):115–124.
    Mou D D,Liu L. 2019. Comparative study of ELM and SVM in hyperspectral image supervision classification[J]. Remote Sensing Technology and Application,34(1):115–124 (in Chinese).
    王东明,许立红. 2016. 破坏建筑物遥感信息提取技术:以尼泊尔8.1级地震为例[J]. 自然灾害学报,25(3):124–129.
    Wang D M,Xu L H. 2016. Extraction technology of remote sensing information on building damage:A case study of MS8.1 Nepal earthquake[J]. Journal of Natural Disasters,25(3):124–129 (in Chinese).
    吴剑. 2010. 基于面向对象技术的遥感震害信息提取与评价方法研究[D]. 武汉: 武汉大学: 1–5.
    Wu J. 2010. The Research of Earthquake Information Extraction and Assessment Based on Object-Oriented Technology with Remotely-Sensed Data[D]. Wuhan: Wuhan University: 1–5 （in Chinese）.
    游永发,王思远,王斌,马元旭,申明,刘卫华,肖琳. 2019. 高分辨率遥感影像建筑物分级提取[J]. 遥感学报,23(1):125–136.
    You Y F,Wang S Y,Wang B,Ma Y X,Shen M,Liu W H,Xiao L. 2019. Study on hierarchical building extraction from high resolution remote sensing imagery[J]. Journal of Remote Sensing,23(1):125–136 (in Chinese).
    张峰,薛艳丽,李英成,丁晓波. 2008. 基于SVM的多源遥感影像面向对象建筑物提取方法[J]. 国土资源遥感,47(2):27–29.
    Zhang F,Xue Y L,Li Y C,Ding X B. 2008. Object-oriented building extraction of multi-source remote sensing imagery based on SVM[J]. Remote Sensing for Land &Resources,47(2):27–29 (in Chinese).
    张景发,李强,焦其松. 2017. 建筑物震害多源遥感特征与机理分析[J]. 地震学报,39(2):257–272. doi: 10.11939/jass.2017.02.009
    Zhang J F,Li Q,Jiao Q S. 2017. Multi-source remote sensing characteristics and mechanism analyses of building seismic damages[J]. Acta Seismologica Sinica,39(2):257–272 (in Chinese).
    赵萍,傅云飞,郑刘根,冯学智,Satyanarayana B. 2005. 基于分类回归树分析的遥感影像土地利用/覆被分类研究[J]. 遥感学报,9(6):708–716. doi: 10.11834/jrs.200506103
    Zhao P,Fu Y F,Zheng L G,Feng X Z,Satyanarayana B. 2005. Cart-based land use/cover classification of remote sensing images[J]. Journal of Remote Sensing,9(6):708–716 (in Chinese).
    赵妍. 2017. 建筑物震害遥感影像面向对象变化检测研究[D]. 北京: 中国地质大学（北京）: 1–5.
    Zhao Y. 2017. The Research of Building Earthquake Damage Change Detection Based on Object-Oriented Technology With Remote Sensing Image[D]. Beijing: China University of Geosciences （Beijing）: 1–5 （in Chinese）.
    Alonso-Benito A,Arroyo L A,Arbelo M,Hernández-Leal P,González-Calvo A. 2013. Pixel and object-based classification approaches for mapping forest fuel types in Tenerife Island from ASTER data[J]. Int J Wildland Fire,22(3):306–317. doi: 10.1071/WF11068
    Baatz M, Schäpe A. 1999. Object-oriented and multi-scale image analysis in semantic networks[C]//Proceedings of the 2nd International Symposium on Operationalization of Remote Sensing. Enschede: 16–20.
    Benediktsson J A,Swain P H,Ersoy O K. 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data[J]. IEEE Trans Geosci Remote Sens,28(4):540–550. doi: 10.1109/TGRS.1990.572944
    Drăguţ L,Tiede D,Levick S R. 2010. ESP:A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data[J]. Int J Geograph Inf Sci,24(6):859–871. doi: 10.1080/13658810903174803
    Drăguţ L,Csillik O,Eisank C,Tiede D. 2014. Automated parameterisation for multi-scale image segmentation on multiple layers[J]. ISPRS J Photogr Remote Sens,88:119–127. doi: 10.1016/j.isprsjprs.2013.11.018
    Hussain M,Chen D M,Cheng A,Wei H,Stanley D. 2013. Change detection from remotely sensed images:From pixel-based to object-based approaches[J]. ISPRS J Photogr Remote Sens,80:91–106. doi: 10.1016/j.isprsjprs.2013.03.006
    Li Q,Gong L X,Zhang J F. 2019. A correlation change detection method integrating PCA and multi-texture features of SAR image for building damage detection[J]. Eur J Remote Sens,52(1):435–447. doi: 10.1080/22797254.2019.1630322
    Li X D,Yang W N,Ao T Q,Li H X,Chen W Q. 2011. An improved approach of information extraction for earthquake-damaged buildings using high-resolution imagery[J]. J Earthq Tsunami,5(4):389–399. doi: 10.1142/S1793431111001157
    Gulsen T K,Musaoglu N,Ersoy O K. 2011. Damage assessment of 2010 Haiti earthquake with post-earthquake satellite image by support vector selection and adaptation[J]. Photogrammetr Eng Remote Sens,77(10):1025–1035. doi: 10.14358/PERS.77.10.1025
    Turker M,Koc-San D. 2015. Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification,hough transformation and perceptual grouping[J]. Int J Appl Earth Observ Geoinform,34:58–69. doi: 10.1016/j.jag.2014.06.016
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