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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (2) : 44-50     DOI: 10.6046/gtzyyg.2019.02.07
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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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.

Keywords damaged building      convolutional neural network      SVM      high-resolution remote sensing image     
:  TP751  
Corresponding Authors: Yunsheng ZHANG     E-mail:
Issue Date: 23 May 2019
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Yunsheng ZHANG
Siyang CHEN
Zhengrong ZOU
Yaochen ZHU
Ruixue ZHAO
Cite this article:   
Yang ZHOU,Yunsheng ZHANG,Siyang CHEN, et al. Disaster damage detection in building areas based on DCNN features[J]. Remote Sensing for Land & Resources, 2019, 31(2): 44-50.
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Fig.1  Structure diagram of DCNN
Fig.2  Flowchart of proposed method
Fig.3  Post-earthquake aerial imageries of Haiti in 2010
Fig.4  Samples of damaged and undamaged areas
Fig.5  Verification sets of damaged areas labeled by manual work
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 training results(%)
Fig.6  Classification results of verification areas
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  Verification set classification results
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