Fine information extraction of 3D building seismic damage based on unmanned aerial vehicle oblique images
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摘要: 无人机倾斜摄影技术建模生成的三维影像较好地展现了建筑物侧面和顶面的震害细节信息，然而影像的高维度特性难以直接基于三维影像提取震害信息，经过降低维度转换的二维纹理影像往往会导致建筑物震害信息的不完整性和破碎性。针对这些问题，本文以2017年九寨沟MS7.0地震为例，提出了一种直接从九寨沟震后三维影像获取侧面纹理信息的方法，即将三维模型打散，实现纹理与不规则三角网分离，从而获取完整的纹理影像，然后利用金字塔模型的瓦片坐标范围、瓦片命名规则和建筑物单体的空间位置选取最优纹理影像，再使用加权均值方差法确定纹理影像中建筑物的外墙最佳分割尺度后，采用面向对象方法提取建筑物外墙和墙皮脱落信息，最后通过对这些建筑物震害特征的分析，判定单体建筑物的破坏等级。结果显示，该方法成功获取了建筑物完整的侧面震害纹理影像，并基于纹理影像提取了外墙、裂缝和墙皮脱落区域信息判定建筑物单体为中等、严重两个破坏等级。Abstract: The three-dimensional image generated by the unmanned aerial vehicle oblique photography technology can better display the details of seismic damage on the side and top of buildings. However, it is difficult to directly extract the seismic damage information based on the three-dimensional image due to the high latitude characteristics of the image, and the two-dimensional texture image transformed by reducing the dimension often leads to the incompleteness and fragmentation of the seismic damage information of buildings. To solve these problems, this paper takes the 2017 Jiuzhaigou MS7.0 earthquake as an example, and proposes a method for scattering the three-dimensional model, separating texture image from triangulated irregular network, and directly obtaining the complete side texture image after the Jiuzhaigou earthquake. Then, the optimal texture image is selected by using the tile coordinate range of pyramid model, the naming rules of tile and the spatial position of building monomer. After the optimal segmentation scale of building exterior wall in texture image is determined by using weighted mean variance method, this paper adopts the object-oriented method to extract the information of building exterior wall and wall skin shedding. Finally, through the analysis of earthquake damage characteristics of these buildings, the damage level of building monomer is determined. The results show that the method successfully obtains the complete side seismic damage texture images of buildings, and extracts the information of the external wall, crack and wall peeling area based on the texture image to determine the medium and serious damage levels of the building monomer.
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图 1 千古情风景区建筑物的三维模型和纹理影像
（a） 20级三维模型；（b） 21级三维模型；（c） 20级纹理影像；（d） 21级纹理影像
Figure 1. Three-dimensional models and texture images of building in Qianguqing scenic spot
（a） 20-level 3D model；（b） 21-level 3D model；（c） 20-level texture image；（d） 21-level texture image
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图 2 分割尺度分别为30 （a），90 （b），50 （c）和70 （d）的千古情风景区建筑物的纹理影像
Figure 2. Texture images of the building in Qianguqing scenic spot with scales of 30 （a），90 （b），50 （c） and 70 （d）
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图 3 对象加权均值方差随分割尺度的变化图
Figure 3. Variation of weighted mean variance of object with segmentation scale
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图 4 千古情风景区建筑物震害信息提取结果
图（d）中蓝色表示墙皮脱落信息，红色表示外墙，青色表示裂缝，图5d和6d与此相同（a） 航拍照片；（b） 三维模型；（c） 纹理影像；（d） 震害信息提取结果
Figure 4. Extraction results of building seismic damage information in Qianguqing scenic spot
In Fig. （d），blue indicates the wall peeling information，red indicates the exterior wall，and cyan indicates the crack，which are the same in Figs. 5d and 6d. （a） Aerial photography；（b） 3D model；（c） Texture image；（d） Extraction results of seismic damage information
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图 5 漳扎镇邮政储蓄银行震害信息提取结果
（a） 航拍照片；（b） 三维模型；（c） 纹理影像；（d） 震害信息提取结果
Figure 5. Extraction results of building seismic damage information in Zhangzha town postal savings bank
（a） Aerial photography；（b） 3D model；（c） Texture image；（d） Extraction results of seismic damage information
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图 6 藏式碉楼建筑物震害信息提取结果
（a） 航拍照片；（b） 三维模型；（c） 纹理影像；（d） 震害信息提取结果
Figure 6. Seismic damage information extraction results of Tibetan watchtower building
（a） Aerial photography；（b） 3D model；（c） Texture image；（d） Extraction results of seismic damage information
表 1 规则集特征参数及阈值
Table 1 Feature parameters and thresholds of rule sets
建筑物震害信息提取对象 特征参数及其阈值 外墙面 $\tfrac{{\overline R }}{{\overline R {\simfont\text{＋}} \overline G {\simfont\text{＋}} \overline B }}$ ＞0.42，72＜
$\tfrac{{{\overline{R}} {\simfont\text{＋}} {\overline{B}} {\simfont\text{＋}} {\overline{G}} }}{3}$ ＜144，267＜A＜543，0.5＜C＜0.8，7＜GLDV＜32
墙皮脱落处 0.56＜Y＜1，−41＜2 $ {\overline { G}} $ －
${\overline { B}} $ －
${\overline { R}} $ ＜−28，16＜GLDV＜24，1.8＜S＜2.6，1.3＜C＜2.3
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