基于最优分割的高分辨率遥感影像震害建筑物识别技术

杜妍开, 龚丽霞, 李强, 张景发

杜妍开, 龚丽霞, 李强, 张景发. 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. 地震学报, 42(6): 760-768. DOI: 10.11939/jass.20200027
引用本文: 杜妍开, 龚丽霞, 李强, 张景发. 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. 地震学报, 42(6): 760-768. DOI: 10.11939/jass.20200027
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

基于最优分割的高分辨率遥感影像震害建筑物识别技术

基金项目: 中国地震局地壳应力研究所中央级公益性科研院所基本科研业务费专项(ZDJ2018-14)资助
详细信息
    通讯作者:

    龚丽霞: e-mail:xiaolongzhu1900@hotmail.com

  • 中图分类号: P237

Earthquake damage building identification technology based on high resolution remote sensing image with optimal segmentation

  • 摘要: 为了提高建筑物震害信息提取的效率与准确度,针对震后高分辨率遥感影像,根据震害建筑物在遥感影像上的特征,以2010年海地MS7.0地震为例,通过尺度参数估计算法自动选择最优分割尺度对影像进行多尺度分割,并采用面向对象方法对海地高分辨率遥感影像进行建筑物震害信息提取,同时与基于像元的支持向量机、反向传播神经网络、基于分类回归算法的决策树分类方法进行比较。试验结果表明,面向对象的分类方法具有更好的目视效果和更高的分类精度,有利于地震后震害信息的准确提取和快速评估。
    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.
  • 随着城镇化建设的发展,土地资源日渐紧缺,对地下空间的开发和利用不断深化。然而,地下结构的建设破坏了土体的局部整体性,改变了原场地的动力特性,因而对附近地上和地下工程结构的抗震安全造成了影响。因此,系统深入地分析由于地下结构的存在而引起的沿线地震动随深度的变化规律,对地下结构沿线的结构抗震设计具有十分重要的意义。

    地下结构对附近场地地震动影响的实质是地下结构对地震波的散射。Mow和Pao (1973)最早运用波函数展开法研究了无限空间中的隧道在弹性波入射下的动应力集中问题;随之,Lee和Trifunac (1979)运用该方法分析了半空间中衬砌隧道对SH波的动力响应。Lee和Karl (19921993)通过理论分析给出了半空间中单个无衬砌隧道对P波和SV波散射问题的解析解。基于单个衬砌隧道的研究,Liang等(2003)得到了半空间中双衬砌隧道对P波和SV波散射问题的解析解。Liu等(2016)分析了弹性半空间中平面波作用下双垂直衬砌隧道的动力相互作用。Xu等(2011)采用傅里叶-贝塞尔级数展开方法计算了半空间中圆形衬砌隧道对P波入射的动力响应。考虑不同种类弹性波的入射情形,梁建文等(2005ab)研究了地下圆形隧道对地表运动幅值的影响。Liu等(2013)考察了弹性半空间中隧道处于浅埋时平面 P-SV 波和瑞雷波的动力响应。Luco和de Barros (2010)以及de Barros和Luco (2010)计算了水平层状半空间中埋置隧道在入射体波下的三维动力响应。对于平面SV波和P波垂直入射的情形,Oliaei和Alitalesh (2015)分析了由于地下圆形和椭圆形隧道的存在而引起的地面位移被放大的现象。利用四阶有限差分方法,Narayan等(2015)探讨了瑞雷波入射下地下无衬砌隧道和有衬砌隧道对其周围应变和黏弹性地基地表位移的影响。Alielahi和Adampira (2016)应用边界元法给出了P波和SV波入射时双平行隧道对其周围垂直平面内地震动响应的影响。Liu和Liu (2015)利用间接边界元法讨论了弹性楔形空间中隧道在SH波入射下对附近表面地震动的影响。Parvanova等(2014)利用数值模拟方法探讨了局部地形对隧道动力响应的影响。

    目前,大部分研究成果仅针对SH波入射情况下地下隧道对地表面地震动的影响(Liang et al,20122013付佳等,2016),而且对考虑地下隧道周围土体在一定深度范围内的动力响应研究也较少。为此,本文拟以含有圆形衬砌隧道的弹性半空间为研究对象,分析地下圆形隧道对场地动力响应的影响,重点研究隧道埋深、衬砌刚度、入射角度以及入射频率对地下隧道周围土体位移振动幅值随深度的变化规律,以期为定量评估地下隧道对既有地下建筑物地震安全性提供理论依据。

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    图  1  弹性半空间的隧道模型
    Figure  1.  Model of tunnel in elastic half space

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    $ w_1^i({r_1}{\text{,}} {\theta _1}) {\text{=}} \sum\limits_{m {\text{=}} 0}^{ {\text{+}} \infty } {{\varepsilon _m}} {({\text{-}}{\rm{i}})^m}{{\rm J}_m}(k{r_1})(\cos m\gamma \cos m{\theta _1} {\text{+}} \sin m\gamma \sin m{\theta _1}){\text{,}} $

    (1)

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    图  2  镜像法求解
    Figure  2.  Solving model by image method

    $ w_2^i({r_2}{\text{,}} {\theta _2}) {\text{=}} \sum\limits_{m {\text{=}} 0}^{ {\text{+}} \infty } {{\varepsilon _m}} {({\text{-}} {\rm{i}})^m}{{\rm J}_m}(k{r_2})(\cos m\gamma \cos m{\theta _2} {\text{+}} \sin m\gamma \sin m{\theta _2}){\text{.}} $

    (2)

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    $ w_1^r\left({{r_1}{\text{,}} {\theta _1}} \right) {\text{=}} \sum\limits_{m {\text{=}} 0}^{ {\text{+}} \infty } {{\rm H}_m^{\left(2 \right)}} \left({k{r_1}} \right)\left({{A_m}\cos m{\theta _1} {\text{+}} {B_m}\sin m{\theta _1}} \right){\text{,}} $

    (3)

    $ w_2^r\left({{r_2}{\text{,}} {\theta _2}} \right) {\text{=}} \sum\limits_{n {\text{=}} 0}^{ {\text{+}} \infty } {{\rm H}_n^{\left(2 \right)}} \left({k{r_2}} \right)\left({{A_n}\cos n{\theta _2} {\text{+}}{B_n}\sin n{\theta _2}} \right){\text{,}} $

    (4)

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    $ w_1^f\left({{r_1}{\text{,}} {\theta _1}} \right) {\text{=}} \sum\limits_{m {\text{=}} 0}^{ {\text{+}} \infty } {{\rm H}_m^{\left(2 \right)}} \left({{k_1}{r_1}} \right)\left({C_m^{(2)}\cos m{\theta _1} {\text{+}} D_m^{(2)}\sin m{\theta _1}} \right){\text{,}} $

    (5)

    $ w_2^f\left({{r_1} {\text{,}} {\theta _1}} \right) {\text{=}}\sum\limits_{m {\text{=}} 0}^{ {\text{+}} \infty } {{\rm H}_m^{\left(1 \right)}} \left({{k_1}{r_1}} \right)\left({C_m^{(1)}\cos m{\theta _1} {\text{+}} D_m^{(1)}\sin m{\theta _1}} \right) {\text{,}} $

    (6)

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    因此,在SH波入射的情形下,半空间中波的势函数为

    $ {w^d} {\text{=}} w_1^i {\text{+}} w_2^i {\text{+}} w_1^r {\text{+}} w_2^r{\text{,}} $

    (7)

    衬砌介质中波的势函数为

    $ {w^f} {\text{=}} w_1^f {\text{+}} w_2^f{\text{.}} $

    (8)

    引入衬砌隧道的边界条件:

    $ {\sigma _{rz}} {\text{=}} {\mu _1}\frac{{\partial {w^f}}}{{\partial {r_1}}} {\text{=}} 0 {\text{,}}{r_1} {\text{=}} b {\text{,}} $

    (9)

    $ {w^d}{\text{=}} {w^f} {\text{,}} {r_1} {\text{=}} a {\text{,}} $

    (10)

    $ {\mu _0}\frac{{\partial {w^d}}}{{\partial {r_1}}} {\text{=}} {\mu _1}\frac{{\partial {w^f}}}{{\partial {r_1}}}{\text{,}} {r_1}{\text{=}} a{\text{,}} $

    (11)

    即可求得式(3)(6)中所有待定系数,从而确定式(7)中半空间介质内波的势函数。在SH波作用下,沿深度方向的位移可通过求解以上边界条件得出,从而求得隧道周围沿深度方向的位移幅值,具体求解过程不再赘述。

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    图  3  SH波入射下的地表位移幅值
    Figure  3.  Surface displacement amplitude for SH waves incidence

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    表  1  距地表6a深度范围内隧道左右两侧最大地下位移幅值
    Table  1.  The maximum amplitude of underground displacement on both sides of tunnel within a depth of 6a from surface
    入射角/°地下位移幅值
    x/a=−3.0x/a=−1.5x/a=1.5x/a=3.0
    02.662.792.792.66
    302.902.832.312.45
    603.302.372.642.61
    903.313.943.373.01
    下载: 导出CSV 
    | 显示表格
    图  4  隧道两侧SH波从不同角度入射时的地下位移幅值变化曲线
    Figure  4. 

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    图  5  不同隧道埋深时隧道两侧的SH波地下位移幅值变化
    Figure  5.  Variation of underground displacement amplitude with D/a for SH waves on both sides of the tunnel

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    图  6  不同入射频率时隧道两侧的SH波地下位移幅值变化曲线
    Figure  6.  Variation of underground displacement amplitude with η for SH waves on both sides of the tunnel

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    图  7  不同隧道衬砌刚度隧道两侧的SH波地下位移幅值变化
    Figure  7.  Variations of underground displacement amplitude with lining stiffness for SH waves on both sides of the tunnel

    本文应用波函数展开法和镜像法,得到了平面SH波作用下含圆形衬砌隧道的弹性半空间中散射波场的级数解答。通过数值算例分析,研究了平面SH的入射角度、入射频率和隧道埋深、衬砌刚度对沿线地下地震动的影响。结果表明,地下隧道的存在对其周围地震动具有显著的影响,并具有如下规律:

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    2) 随着入射频率的增加,隧道周围土体的位移幅值有逐渐增大的趋势,但是在隧道左侧近处存在异常区,该区随着频率的增加地下位移振幅逐渐减小。

    3) 在隧道周围近距离处,衬砌刚度的变化对地下位移幅值的影响显著,而在隧道远距离处,衬砌刚度对地下位移幅值的影响较弱。

  • 图  1   主要技术流程

    Figure  1.   Main technical flow

    图  2   基于ESP算法的最优尺度分割评估结果

    Figure  2.   Evaluation results of optimal segmentation scale based on ESP algorithm

    图  3   实地调查的矢量结果

    Figure  3.   Vector results of field investigation

    图  4   研究区位置示意图

    Figure  4.   Location of the studied area

    图  5   基于面向对象分类结果

    Figure  5.   Classification results based on object-oriented

    图  6   基于各种方法的“噪声”现象对比

    Figure  6.   Comparison of “noise” phenomena based on various methods

    图  7   BP神经网络(a)、SVM(b)、基于CART算法决策树(c)分类结果

    Figure  7.   Classification results of BP neural network (a),SVM (b),decision tree based on CART algorithm method (c)

    表  1   各地物的特征规则集

    Table  1   The feature rule set of a variety of surface features

    地物类别特征规则集
    阴影亮度≤23
    植被均值≤30.5;矩形度≤0.9;紧致度≥1.61
    完全损毁建筑物GLCM角二阶矩≤0.002;GLCM同质性≤0.06;标准差≥30
    基本完好建筑物标准差≤12或标准差≥20;GLCM熵≥9;4.1≤GLDV熵≤4.3
    1.925≤密度≤2.222或1.19≤密度≤1.39
    中度破坏建筑物还未分类
    注:GLCM为灰度共生矩阵,GLDV为灰度差分矢量。
    下载: 导出CSV

    表  2   矢量分类结果中建筑物数量

    Table  2   The number of buildings achieved from the vector classification results

    建筑物类型实地调查所得数据个数面向对象分类所得数据个数
    基本完好建筑物274194
    中度破坏建筑物237308
    完全损毁建筑物235212
    总计746714
    下载: 导出CSV

    表  3   各分类方法的精度评价

    Table  3   Accuracy evaluation of various classification methods

    方法总体精度Kappa系数方法总体精度Kappa系数
    SVM 70.37% 0.5966 基于CART算法决策树 76.22% 0.633 0
    BP神经网络 74.96% 0.6537 面向对象 87.10% 0.819 3
    下载: 导出CSV

    表  4   各分类方法的错分率

    Table  4   The error rate of various classification methods

    方法基本完好中度破坏完全损毁
    SVM36.73%33.22%18.62%
    BP神经网络30.53%30.24%16.00%
    基于CART算法决策树37.00%40.44%9.08%
    面向对象20.00%5.56%14.29%
    下载: 导出CSV
  • 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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出版历程
  • 收稿日期:  2020-02-23
  • 修回日期:  2020-05-28
  • 网络出版日期:  2021-02-06
  • 发布日期:  2020-11-14

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