Disaster damage detection in building areas based on DCNN features

doi:10.6046/gtzyyg.2019.02.07

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(5779 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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.

Keywords damaged building convolutional neural network SVM high-resolution remote sensing image

TP751

Corresponding Authors: Yunsheng ZHANG E-mail: zhangys@csu.edu.cn

Issue Date: 23 May 2019

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Yang ZHOU
	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.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.02.07 OR https://www.gtzyyg.com/EN/Y2019/V31/I2/44

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

Tab.1 SVM training results(%)

Fig.6 Classification results of verification areas

Tab.2 Verification set classification results

[1]	Janalipour M, Mohammadzadeh A . Building damage detection using object-based image analysis and ANFIS from high-resolution image (Case study:BAM earthquake,Iran)[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016,9(5):1937-1945. doi: 10.1109/JSTARS.2015.2458582 url: http://ieeexplore.ieee.org/document/7182741/
[2]	郭华东, 鹿琳琳, 马建文 , 等. 一种改进的地震灾害倒塌房屋遥感信息自动识别方法[J]. 科学通报, 2009,54(17):2581-2585.
[2]	Guo H D, Lu L L, Ma J W , et al. An improved method for automatic recognition of remote sensing information of earthquake disasters with collapsed houses[J]. Chinese Science Bulletin, 2009,54(17):2581-2585.
[3]	叶昕, 王俊, 秦其明 . 基于高分一号卫星遥感图像的建筑物震害损毁检测研究——以2015年尼泊尔M_S8.1地震为例[J]. 地震学报, 2016,38(3):477-485.
[3]	Ye X, Wang J, Qin Q M . Building seismic damage detection research based on high-resolution satellite remote sensing images:A case study of Nepal M_S8.1 earthquake in 2015[J]. Journal of Seismology, 2016,38(3):477-485.
[4]	Yamazaki F, Suzuki D, Maruyama Y. Detection of damages due to earthquakes using digital aerial images [C]//The 14th World Conference on Earthquake Engineering, 2008.
[5]	Ehrlich D, Bielski C. Texture based change detection of built-up on SPOT panchromatic imagery using PCA [C]//Urban Remote Sensing Event.IEEE, 2011: 77-80.
[6]	于海洋, 程钢, 张育民 , 等. 基于LiDAR和航空影像的地震灾害倒塌建筑物信息提取[J]. 国土资源遥感, 2011,23(3):77-81.doi: 10.6046/gtzyyg.2011.03.14. doi: 10.6046/gtzyyg.2011.03.14
[6]	Yu H Y, Cheng G, Zhang Y M , et al. Earthquake disaster collapse building information extraction based on LiDAR and aerial images[J]. Remote Sensing for Land and Resources, 2011,23(3):77-81.doi: 10.6046/gtzyyg.2011.03.14.
[7]	董燕生, 潘耀忠, 方伟华 , 等. 基于面向对象技术的建筑物震害识别方法研究[J]. 地震研究, 2011,34(3):372-377.
[7]	Dong Y S, Pan Y Z, Fang W H , et al. Research on building earthquake damage identification method based on object-oriented technology[J]. Earthquake Research, 2011,34(3):372-377.
[8]	王岩, 王晓青, 窦爱霞 . 面向对象遥感分类方法在汶川地震震害提取中的应用[J]. 地震, 2009,29(3):54-60.
[8]	Wang Y, Wang X Q, Dou A X . Application of object-oriented remote sensing classification method in seismic damage extraction of Wenchuan earthquake[J]. Earthquake, 2009,29(3):54-60.
[9]	王慧敏, 李艳 . 面向对象的损毁建筑物提取[J].遥感信息, 2011 (5):81-85.
[9]	Wang H M, Li Y . Object-oriented damaged building extraction[J].Remote Sensing Information, 2011 (5):81-85.
[10]	刘宇, 曹国, 周丽存 , 等. 基于多特征结合的损毁建筑物检测[J]. 计算机应用, 2015,35(9):2652-2655. doi: 10.11772/j.issn.1001-9081.2015.09.2652
[10]	Liu Y, Cao G, Zhou L C , et al. Damaged building detection based on multi-features combination[J]. Computer Application, 2015,35(9):2652-2655.
[11]	许夙晖, 慕晓冬, 赵鹏 , 等. 利用多尺度特征与深度网络对遥感影像进行场景分类[J]. 测绘学报, 2016,45(7):834-840.
[11]	Xu S H, Mu X D, Zhao P , et al. Multi-scale feature and deep network for scene classification of remote sensing images[J]. Journal of Surveying and Mapping, 2016,45(7):834-840.
[12]	Hu F, Xia G S, Hu J , et al. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery[J]. Remote Sensing, 2015,7(11):14680-14707. doi: 10.3390/rs71114680 url: http://www.mdpi.com/2072-4292/7/11/14680
[13]	Zhou W, Newsam S, Li C , et al. Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval[J]. Remote Sensing, 2017,9(5):489. doi: 10.3390/rs9050489 url: http://www.mdpi.com/2072-4292/9/5/489
[14]	Vetrivel A, Gerke M, Kerle N , et al. Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images,and multiple-kernel-learning[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017,140:45-59.
[15]	李奇峰 . 结合多特征描述和SVM的遥感影像分类研究[D]. 郑州:郑州大学, 2015.
[15]	Li Q F . Research on Remote Sensing Image Classification Based on Multiple Feature Description and SVM[D]. Zhengzhou:Zhengzhou University, 2015.
[16]	王旭东, 段福洲, 屈新原 , 等. 面向对象和SVM结合的无人机数据建筑物提取[J]. 国土资源遥感, 2017,29(1):97-103.doi: 10.6046/gtzyyg.2017.01.15.
[16]	Wang X D, Duan F Z, Qu X Y , et al. UAV data architecture extraction combined with object-oriented and SVM[J]. Remote Sensing for Land and Resources, 2017,29(1):97-103.doi: 10.6046/gtzyyg.2017.01.15.
[17]	张峰, 薛艳丽, 李英成 , 等. 基于SVM的多源遥感影像面向对象建筑物提取方法[J]. 国土资源遥感, 2008,20(2):27-29.doi: 10.6046/gtzyyg.2008.02.07. doi: 10.6046/gtzyyg.2008.02.07
[17]	Zhang F, Xue Y L, Li Y C , et al. Multi-source remote sensing image-based object-oriented building extraction method based on SVM[J]. Remote Sensing for Land and Resources, 2008,20(2):27-29.doi: 10.6046/gtzyyg.2008.02.07.
[18]	Dalal N, Triggs B. Histograms of oriented gradients for human detection [C]//2005 IEEE Computer Society Conference on Computer Vision and Patter Recognition, 2005: 886-893.
[19]	Simonyan K , Zisserman A .Very deep convolutional networks for large-scale image recognition[EB/OL].. url: https://arxiv.org/pdf/1409.1556.pdf
[20]	Penatti O A B, Nogueira K, Santos J A D. Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? [C]//Computer Vision and Pattern Recognition Workshops.IEEE, 2015: 44-51.
[21]	Wang J, Luo C, Huang H , et al. Transferring pre-trained deep CNNs for remote scene classification with general features learned from linear PCA network[J]. Remote Sensing, 2017,9(3):225. doi: 10.3390/rs9030225 url: http://www.mdpi.com/2072-4292/9/3/225
[22]	Cortes C, Vapnik V N . Support-vector networks[J]. Machine Learning, 1995,20(3):273-297.
[23]	Xia G S, Hu J, Hu F , et al. AID:A benchmark data set for performance evaluation of aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017,55(7):3965-3981. doi: 10.1109/TGRS.2017.2685945 url: http://ieeexplore.ieee.org/document/7907303/

[1]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[2]	YU Xinli, SONG Yan, YANG Miao, HUANG Lei, ZHANG Yanjie. Multi-model and multi-scale scene recognition of shipbuilding enterprises based on convolutional neural network with spatial constraints[J]. Remote Sensing for Natural Resources, 2021, 33(4): 72-81.
[3]	WANG Hua, LI Weiwei, LI Zhigang, CHEN Xueye, SUN Le. Hyperspectral image classification based on multiscale superpixels[J]. Remote Sensing for Natural Resources, 2021, 33(3): 63-71.
[4]	QIU Yifan, CHAI Dengfeng. A deep learning method for Landsat image cloud detection without manually labeled data[J]. Remote Sensing for Land & Resources, 2021, 33(1): 102-107.
[5]	LIU Zhao, ZHAO Tong, LIAO Feifan, LI Shuai, LI Haiyang. Research and comparative analysis on urban built-up area extraction methods from high-resolution remote sensing image based on semantic segmentation network[J]. Remote Sensing for Land & Resources, 2021, 33(1): 45-53.
[6]	WANG Xiaolong, YAN Haowen, ZHOU Liang, ZHANG Liming, DANG Xuewei. Using SVM classify Landsat image to analyze the spatial and temporal characteristics of main urban expansion analysis in Democratic People’s Republic of Korea[J]. Remote Sensing for Land & Resources, 2020, 32(4): 163-171.
[7]	WEI Hongyu, ZHAO Yindi, DONG Jihong. Cooling tower detection based on the improved RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(4): 68-73.
[8]	LIU Zhao, LIAO Feifan, ZHAO Tong. Remote sensing image urban built-up area extraction and optimization method based on PSPNet[J]. Remote Sensing for Land & Resources, 2020, 32(4): 84-89.
[9]	CAI Yaotong, LIU Shutong, LIN Hui, ZHANG Meng. Extraction of paddy rice based on convolutional neural network using multi-source remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(4): 97-104.
[10]	CAI Zhiling, WENG Qian, YE Shaozhen, JIAN Cairen. Remote sensing image scene classification based on Inception-V3[J]. Remote Sensing for Land & Resources, 2020, 32(3): 80-89.
[11]	WU Tong, PENG Ling, HU Yuan. Informal garbage dumps detection in high resolution remote sensing images based on SU-RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(3): 90-97.
[12]	Jinjie KANG, Haoping QI, Qinghua YANG, Hua CHEN. Study of remote sensing detection method for road obstacle and accessibility evaluation[J]. Remote Sensing for Land & Resources, 2020, 32(2): 94-102.
[13]	Peiqing LOU, Xiaoyu CHEN, Shutong WANG, Bolin FU, Yongyi HUANG, Tingyuan TANG, Ming LING. Object recognition of karst farming area based on UAV image: A case study of Guilin[J]. Remote Sensing for Land & Resources, 2020, 32(1): 216-223.
[14]	Famao YE, Wei LUO, Yanfei SU, Xuqing ZHAO, Hui XIAO, Weidong MIN. Application of convolutional neural network feature to remote sensing image registration[J]. Remote Sensing for Land & Resources, 2019, 31(2): 32-37.
[15]	Juan FENG, Jianli DING, Wenyu WEI. Soil salinization monitoring based on Radar data[J]. Remote Sensing for Land & Resources, 2019, 31(1): 195-203.

Viewed

Full text

Abstract

Cited

Shared

Discussed