[1]WANG Zhi,LIU Chao,LIU Xiuju,et al.Study on the object-based classification of low-altitude UAV remote sensing image of the Lushan earthquake based on the SEaTH algorithm[J].Journal of Seismological Research,2018,41(02):173-179.
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Study on the object-based classification of low-altitude UAV remote sensing image of the Lushan earthquake based on the SEaTH algorithm

Journal of Seismological Research[ISSN 1000-0666/CN 53-1062/P] Volume: 41 Number of periods: 2018 02 Page number: 173-179 Column: Public date: 2018-04-20
Title:
Study on the object-based classification of low-altitude UAV remote sensing image of the Lushan earthquake based on the SEaTH algorithm
Author(s):
WANG Zhi1; 2; LIU Chao1; 2; LIU Xiuju3; LU Heng1; 2; CAI Shixiang1; 2; YANG Zhengli1; 2
(1. State Key Laboratory of Hydraulics and Mountain River Engineering,Sichuan University,Chengdu 610065,Sichuan,China)(2. College of Hydraulic and Hydroelectric Engineering,Sichuan University,Chengdu 610065,Sichuan,China)(3. Chengdu Planning Information Technology Center,Chengdu 610042,Sichuan,China)
Keywords:
object-oriented classification;  multiresolution segmentation;  the SEaTH algorithm;  UAV images
CLC:
P315.9; TP751
DOI:
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Abstract:
Before an earthquake that is believed to be a major natural disaster,how to quickly and efficiently extract the area of land use information from remote sensing images,plays a role in the evaluation of the disaster and post-disaster reconstruction. In this study,we select UAV images of the Lushan earthquake stricken areas in Sichuan province as data sources and apply an object-oriented image analysis method. Firstly,the selection of parameters in multi-scale segmentation is studied,and the optimal segmentation parameters are obtained. Then,the digital eigenvalue of each image object is considered,and the improved SEaTH algorithm is used to optimize the eigenvalue optimization. Finally,the classification of low-altitude remote sensing images in the Lushan earthquake area is obtained by using the method of membership information extraction,and the classification accuracy is evaluated. The results show that the total accuracy of the classification is 87.5%,and the Kappa coefficient is 0.835. Through the study of this paper,it can provide technical support for the rapid acquisition of geospatial source data of earthquake disaster areas.
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