The study on extraction of seismic damage of buildings from remote sensing image based on fully convolutional neural network
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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。Abstract: 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.
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图 1 全卷积神经网络结构图（Long等，2015）
Figure 1. Structure diagram of skip-layers of fully convolutional neural network(Long等, 2015)
图 2 研究区震后遥感影像及选取的训练样本分布示意
Figure 2. Remote sensing image and training sample distribution in the research area
图 3 震后高分遥感影像图斑及对应的真值
Figure 3. training samples: post-earthquake high-resolution remote sensing image patch and the corresponding ground truth
图 5 研究区遥感影像及验证样本图斑分布图
Figure 5. Remote sensing image and test sample distribution in the research area
图 6 基于全卷积神经网络提取的建筑物震害信息结果示例图
Figure 6. The typical result showing seismic damage of buildings extracted from RS image by FCN
表 1 基于全卷积神经网络的建筑物震害提取结果混淆矩阵
Table 1. The obfuscation matrix of building damage extraction results based on full convolutional neural network
背景 倒塌建筑物 未倒塌建筑物 合计 正确率 背景 5098760 309868 299996 5708624 0.893 倒塌建筑物 550951 1042364 19899 1613214 0.646 未倒塌建筑物 308373 19246 850543 1178162 0.722 合计 5958084 1371478 1170438 8500000 
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表 2 基于cart监督分类的建筑物震害提取结果混淆矩阵
Table 2. confusion matrix of building damage extraction results based on cart supervised classification
背景 倒塌建筑物 未倒塌建筑物 合计 正确率 背景 3400364 1744432 563828 5708624 0.596 倒塌建筑物 206370 1366026 40818 1613214 0.847 未倒塌建筑物 366833 234979 576350 1178162 0.490 合计 3973567 3345437 1180996 8500000
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