多尺度分割和深度学习相结合的倾斜摄影三维影像建筑物震害信息提取

帅向华 荆帅军 郑向向 刘钦

帅向华,荆帅军,郑向向,刘钦. 2022. 多尺度分割和深度学习相结合的倾斜摄影三维影像建筑物震害信息提取. 地震学报,44(5):881−890 doi: 10.11939/jass.20220105
引用本文: 帅向华,荆帅军,郑向向,刘钦. 2022. 多尺度分割和深度学习相结合的倾斜摄影三维影像建筑物震害信息提取. 地震学报,44(5):881−890 doi: 10.11939/jass.20220105
Shuai X H,Jing S J,Zheng X X,Liu Q. 2022. Extraction of seismic damage information of buildings from three-dimensional images of oblique photography based on multi-scale segmentation and deep learning. Acta Seismologica Sinica,44(5):881−890 doi: 10.11939/jass.20220105
Citation: Shuai X H,Jing S J,Zheng X X,Liu Q. 2022. Extraction of seismic damage information of buildings from three-dimensional images of oblique photography based on multi-scale segmentation and deep learning. Acta Seismologica Sinica44(5):881−890 doi: 10.11939/jass.20220105

多尺度分割和深度学习相结合的倾斜摄影三维影像建筑物震害信息提取

doi: 10.11939/jass.20220105
基金项目: 国家重点研发计划(2018YFC1504500)资助
详细信息
    通讯作者:

    帅向华,硕士,研究员,主要从事灾害风险评估、地震灾害应急与GIS遥感应用方面的研究,e-mail:shuaixhua@sina.com

  • 中图分类号: P315.9

Extraction of seismic damage information of buildings from three-dimensional images of oblique photography based on multi-scale segmentation and deep learning

  • 摘要: 采用多尺度分割和深度学习相结合的方法对震后倾斜摄影三维影像建筑物震害信息进行提取,获取建筑物的屋顶和墙体多种破坏信息。以2017年九寨沟MS7.0地震后倾斜摄影三维影像为例,依据三维影像建筑物顶面和墙体等进行样本的多尺度分割,样本分为完好建筑物面、破坏建筑物面、其它地物和背景等三类,选取211个100×100像素的样本集对卷积神经网络模型进行训练,采用训练后的模型提取灾区千古情风景区和漳扎镇小学的建筑物震害信息,并将提取结果与目视解译结果进行精度对比,结果显示:破坏建筑物面提取精度分别为65.5%和71.1%,总体分类精度分别为82.1%和84.1%,卡帕(Kappa)系数分别为68.7%和64.9%,表明该方法在倾斜摄影三维影像建筑物震害提取方面具有一定的优势。

     

  • 图  1  VGGNet-16网络模型结构,模型下方数字为图像大小(宽×高×深)

    Figure  1.  VGGNet-16 structural framework where the number under the structural is the image size (width×high×deep)

    图  2  千古情风景区的三维模型影像

    Figure  2.  3D model images of Qianguqing scenic spot

    图  3  漳扎镇小学的三维模型影像

    Figure  3.  3D model images of Zhangzha primary school

    图  4  漳扎镇小学的典型建筑物震害表现

    Figure  4.  Seismic damage of typical building in Zhangzha primary school

    图  5  千古情风景区正面的影像分割结果

    Figure  5.  Segmentation result of the front of buildings of Qianguqing sceic spot

    图  6  千古情风景区(a)和漳扎镇小学(b)的建筑物提取结果

    红色代表完好建筑物面,蓝色代表破坏建筑物面,黑色代表其它地物及背景

    Figure  6.  Extraction results of seismic damage of buildings in Qianguqing scenic spot (a) and Zhangzha primary school (b)

    Red represents the intact building surface,blue represents the damaged building surface and black represents other ground objects and backgrounds

    表  1  分类样本集的选取

    Table  1.   Selection of classification sample sets

    标签样本名称样本图片样本个数
    1 完好建筑物面 107
    2 破坏建筑物面 68
    3 其它地物及背景 36
    下载: 导出CSV

    表  2  千古情风景区的建筑物深度学习和目视解译提取结果的混淆矩阵

    Table  2.   Confusion matrix of deep learning and artificial visual extraction results of the buildings in Qianguqing scenic spot

    预测真实破坏建筑物面数完好建筑物面数其它地物及背景面数合计正确率
    破坏建筑物面数53 43620 0848 00781 52765.5%
    完好建筑物面数23 447131 60532 258187 31070.3%
    其它地物及背景数5 00719 001309 974333 98292.8%
    合计81 890170 690350 239602 819
    下载: 导出CSV

    表  3  漳扎镇小学及其周边的深度学习和人工目视解译提取结果混淆矩阵

    Table  3.   Confusion matrix of deep learning and manual visual extraction results of the buildings in Zhangzha primary school and it’s vicinity

    预测真实破坏建筑物面数完好建筑物面数其它地物及背景面数合计正确率
    破坏建筑物面数61 44520 0075 00386 44571.1%
    完好建筑物面数3 668165 57044 093213 33177.6%
    其它地物及背景数4 00896 971688 942789 92187.2%
    合计69 121282 548738 0381 089 697
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-06-17
  • 修回日期:  2022-08-08
  • 网络出版日期:  2022-09-09
  • 刊出日期:  2022-09-15

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