摘要
为解决建筑物震害信息提取自动化程度不高的问题,本文将全卷积神经网络应用于建筑物震害遥感信息提取。以玉树地震后获取的玉树县城区0.2m分辨率航空影像作为建筑物震害信息提取试验数据源,将试验区地物划分为倒塌建筑物、未倒塌建筑物和背景3类。对427个500×500像素的子影像进行人工分类与标注,选取393个组成训练样本集,34个用于验证。利用训练样本集对全卷积神经网络进行训练,采用训练后的网络对验证样本进行建筑物震害信息提取及精度评价。研究结果表明:建筑物震害遥感信息提取总体分类精度为82.3%,全卷积神经网络方法能提高信息提取自动化程度,具有较好的建筑物震害信息提取能力。
In order to solve the problem that the automation degree of extracting damaged Buildings caused by earthquake is not very high,in this paper a fully convolutional neural network is applied to extract the remote sensing information of earthquake damage to buildings.The 0.2m-resolution aerial image of the Yushu County urban area obtained after the Yushu earthquake was used as the data source to test the result of convolutional neural network.The objects in the test area were classified into collapsed buildings,uncollapsed buildings,and background.Classify and label 427 sub-images of 500×500 pixels manually,393 of them were selected as training sample set,and others as verification sample set.The training sample set is used to train the full convolutional neural network and the trained network is used to extract the building seismic damage information and evaluate the accuracy based on the verification sample.The result shows that the overall classification accuracy is 82.3%,and the fully convolutional neural network can improve the automation of information extraction and has a better ability to extract building seismic damage information.
作者
陈梦
王晓青
Chen Meng;Wang Xiaoqing(Institute of Earthquake Forecasting,CEA,Beijing 100036,China)
出处
《震灾防御技术》
CSCD
北大核心
2019年第4期810-820,共11页
Technology for Earthquake Disaster Prevention
基金
科技部重点研发课题(2017YFB0504104).
关键词
深度学习
全卷积神经网络
建筑物
震害信息
遥感
Deep learning
Fully convolutional neural network
Buildings
Seismic damage information
Remote sensing
