| 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 |
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| 引用本文： | 帅向华,荆帅军,郑向向,刘钦.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[J].地震学报,2022,44(5):881-890. |
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| 作者姓名： | 帅向华 荆帅军 郑向向 刘钦 |
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| 作者单位： | 1.中国北京 100045 中国地震台网中心 |
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| 基金项目： | 国家重点研发计划（2018YFC1504500）资助 |
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| 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。
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| 关 键 词： | 建筑物震害提取 多尺度分割 深度学习 倾斜摄影 |
| 收稿时间： | 2022-06-17 |
Extraction of seismic damage information of buildings from three-dimensional images of oblique photography based on multi-scale segmentation and deep learning |
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| Affiliation: | 1.China Earthquake Networks Center，Beijing 100045，China2.Henan Earthquake Agency，Zhengzhou 450016，China3.China Aero Geophysical Survey & Remote Sensing Center for Natural Resources，Beijing 100083，China |
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| Abstract: | The method of combining multi-scale segmentation and deep learning is used to extract the seismic damage information of buildings from three-dimensional images of oblique photography after an earthquake. In this way, the comprehensive damage information of the roof and wall of the building is obtained. Taking the 2017 MS7.0 Jiuzhaigou earthquake as an example, firstly, based on the three-dimensional image of the roof and wall of the building, the multi-scale segmentation of samples are divided into three categories: intact building surface, damaged building surface, other ground objects and background. Secondly, 211 sample set with 100×100 pixel are selected to train the neural convolutional network model. The trained model is used to extract the seismic damage information of buildings in Qianguqing scenic spot and Zhangzha primary school. Finally, the accuracy of the extracted results is compared with the visual interpretation results. The results show that the extraction accuracy of damaged building surface is 65.5% and 71.1% respectively, the overall classification accuracy is 82.1% and 84.1% respectively, and the Kappa coefficient is 68.7% and 64.9% respectively. The result indicates that this method has certain advantages in seismic damage extraction of buildings from three-dimensional images of oblique photography. |
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