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国土资源遥感  2019, Vol. 31 Issue (2): 44-50    DOI: 10.6046/gtzyyg.2019.02.07
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于DCNN特征的建筑物震害损毁区域检测
周阳,张云生(),陈斯飏,邹峥嵘,朱耀晨,赵芮雪
中南大学地球科学与信息物理学院,长沙 410083
Disaster damage detection in building areas based on DCNN features
Yang ZHOU,Yunsheng ZHANG(),Siyang CHEN,Zhengrong ZOU,Yaochen ZHU,Ruixue ZHAO
School of Geosciences and Inof-Physics, Central South University, Changsha 410083, China
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摘要 

为了提高基于高空间分辨率遥感影像的建筑物震害损毁评估精度,引入深度卷积神经网络(deep convolutional neural network,DCNN)模型,提出一种利用DCNN全连接层特征结合支持向量机 (support vector machine,SVM)进行遥感影像建筑物震害损毁区域检测的方法。首先,利用神经网络前馈方式从DCNN全连接层提取训练样本和待检测区域的特征; 然后,基于样本训练SVM分类器; 最后,对待检测区域的所有区块进行分类预测和投票确定是否损毁。以2010年海地地震遥感影像为例,建筑物损毁检测正确率可以达到89%,相比于传统的特征提取方法正确率提高了4%。实验结果表明该方法在建筑物震害损毁检测方面具有一定的应用潜力。

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周阳
张云生
陈斯飏
邹峥嵘
朱耀晨
赵芮雪
关键词 损毁建筑物卷积神经网络SVM高空间分辨率遥感影像    
Abstract

In order to improve the precision of damage assessment of post-earthquake buildings based on remote sensing images, this paper introduces a deep convolutional neural network (DCNN) model that performs well in natural image classification and target detection, and also proposes a method of using DCNN fully-connected layer features combined with support vector machine (SVM) to detect damaged building areas in remote sensing images. Firstly, neural network feed forward is used to extract the features of the training samples and the regions to be detected from the DCNN fully-connected layer; then the SVM classifier is learned based on the training samples; finally, all the blocks in the detection region are subjected to predicting and voting to determine whether they are damaged. The authors used Haiti earthquake remote sensing imagery in 2010 to do verification. The accuracy rate of damage detection in this method can reach 89%. Compared with the traditional feature extraction method, the correct rate is improved by 4%. The experimental results show that this method has a certain potential in the detection of building damage damage.

Key wordsdamaged building    convolutional neural network    SVM    high-resolution remote sensing image
收稿日期: 2018-04-10      出版日期: 2019-05-23
ZTFLH:  TP751  
基金资助:国家重点研发计划项目“一体化综合减灾智能服务系统”(2016YFC0803108);湖南省自然科学基金项目“基于深度学习的倾斜摄影模型建筑物提取与三维重建”共同资助(2018JJ3637)
通讯作者: 张云生     E-mail: zhangys@csu.edu.cn
作者简介: 周 阳(1992-),女,硕士研究生,主要从事数字摄影测量方面的研究。Email: csuzy_smile@163.com。
引用本文:   
周阳,张云生,陈斯飏,邹峥嵘,朱耀晨,赵芮雪. 基于DCNN特征的建筑物震害损毁区域检测[J]. 国土资源遥感, 2019, 31(2): 44-50.
Yang ZHOU,Yunsheng ZHANG,Siyang CHEN,Zhengrong ZOU,Yaochen ZHU,Ruixue ZHAO. Disaster damage detection in building areas based on DCNN features. Remote Sensing for Land & Resources, 2019, 31(2): 44-50.
链接本文:  
http://www.gtzyyg.com/CN/10.6046/gtzyyg.2019.02.07      或      http://www.gtzyyg.com/CN/Y2019/V31/I2/44
Fig.1  DCNN结构
Fig.2  方法流程
Fig.3  2010年海地雅克梅勒地区震后航空影像
Fig.4  损毁区域与未损毁区域样本样例
Fig.5  验证集手工标记真实损毁区域
模型 模型精度 测试精度
AlexNet 97.08 88.33
VGGNet 93.50 91.67
BOW 94.25 84.67
Tab.1  SVM训练结果
Fig.6  验证区域分类结果
模型 VGGNet AlexNet
验证区域 验证区域1 验证区域2 验证区域1 验证区域2
实际检测 损毁 未损毁 总计 损毁 未损毁 总计 损毁 未损毁 总计 损毁 未损毁 总计
损毁 861 129 990 718 166 884 855 291 1146 647 158 805
未损毁 150 1 397 1 547 53 663 716 156 1 235 1 391 124 671 795
总计 1 011 1 526 2 537 771 829 1 600 1 011 1 526 2 537 771 829 1 600
漏检率/% 12.8 6.9 15.4 16.1
误检率/% 9.8 20.0 19.1 19.1
正确率/% 89.0 86.3 82.4 82.4
Tab.2  验证集分类结果
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