首站-论文投稿智能助手
典型文献
Surveillance of pine wilt disease by high resolution satellite
文献摘要:
Pine wilt disease caused by the pinewood nema-tode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China. This destructive disease has the characteristics of bring wide-spread, fast onset, and long incubation time. Most importantly, in China, the fatal-ity rate in pines is as high as 100%. The key to reducing this mortality is how to quickly find the infected trees. We pro-posed a method of automatically identifying infected trees by a convolution neural network and bounding box tool. This method rapidly locates the infected area by classifying and recognizing remote sensing images obtained by high resolu-tion earth observation Satellite. The recognition accuracy of the test data set was 99.4%, and the remote sensing image combined with convolution neural network algorithm can identify and determine the distribution of the infected trees. It can provide strong technical support for the prevention and control of pine wilt disease .
文献关键词:
作者姓名:
Hongwei Zhou;Xinpei Yuan;Huanyu Zhou;Hengyu Shen;Lin Ma;Liping Sun;Guofei Fang;Hong Sun
作者机构:
College of Mechanical and Electrical Engineering, Northeast Forestry University,Harbin 150040, People's Republic of China;General Station of Forest and Grassland Pest Managerment, National Forestry and Grassland Administration, Shenyang 110034,People's Republic of China
引用格式:
[1]Hongwei Zhou;Xinpei Yuan;Huanyu Zhou;Hengyu Shen;Lin Ma;Liping Sun;Guofei Fang;Hong Sun-.Surveillance of pine wilt disease by high resolution satellite)[J].林业研究(英文版),2022(04):1401-1408
A类:
pinewood,nema,tode
B类:
Surveillance,wilt,disease,by,high,resolution,satellite,Pine,caused,Bursaphelenchus,xylophilus,has,led,death,large,number,trees,China,This,destructive,characteristics,bring,wide,spread,fast,onset,long,incubation,Most,importantly,fatal,rate,pines,key,reducing,this,mortality,how,quickly,find,infected,We,posed,method,automatically,identifying,convolution,neural,network,bounding,box,tool,rapidly,locates,area,classifying,recognizing,remote,sensing,images,obtained,earth,observation,Satellite,recognition,accuracy,test,data,was,combined,algorithm,can,determine,distribution,It,provide,strong,technical,support,prevention,control
AB值:
0.566608
相似文献
From statistics to grids: A two-level model to simulate crop pattern dynamics
XIA Tian;WU Wen-bin;ZHOU Qing-bo;Peter H.VERBURG;YANG Peng;HU Qiong;YE Li-ming;ZHU Xiao-juan-Key Laboratory for Geographical Process Analysis&Simulation,Hubei Province/College of Urban&Environmental Science,Central China Normal University,Wuhan 430079,P.R.China;Key Laboratory of Agricultural Remote Sensing,Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,P.R.China;Agricultural Information Institute,Chinese Academy of Agricultural Sciences,Beijing 100081,P.R.China;Institute for Environmental Studies,VU University Amsterdam,Amsterdam 1085,The Netherlands;Department of Geology,Ghent University,Ghent 9000,Belgium;Commercial and Economic Law School,China University of Political Science and Law,Beijing 100088,P.R.China
Detection of oil spill based on CBF-CNN using HY-1C CZI multispectral images
Kai Du;Yi Ma;Zongchen Jiang;Xiaoqing Lu;Junfang Yang-College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao 266590,China;First Institute of Oceanology,Ministry of Natural Resources,Qingdao 266061,China;Technology Innovation Center for Ocean Telemetry,Ministry of Natural Resources,Qingdao 266061,China;National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology,Xi'an 710072,China;School of Electronics and Information Engineering,Harbin Institute of Technology,Harbin 150001,China;National Satellite Ocean Application Service,Beijing 100081,China;College of Oceanography and Space Informatics,China University of Petroleum(East China),Qingdao 266580, China
Long-term reconstruction of flash floods in the Qilian Mountains,China,based on dendrogeomorphic methods
QIE Jia-zhi;ZHANG Yong;TRAPPMANN Daniel;ZHONG Yi-hua;BALLESTEROS-CáNOVAS Juan Antonio;FAVILLIER Adrien;STOFFEL Markus-Key Laboratory of Land Surface Pattern and Simulation,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;Climate Change Impacts and Risks in the Anthropocene(C-CIA),Institute for Environmental Sciences,University of Geneva,Geneva CH-1205,Switzerland;Dendrolab.ch,Department of Earth Sciences,University of Geneva,Geneva CH-1205,Switzerland;National Museum of Natural Sciences,MNCN-CSIC,C/Serrano 115bis,28006,Madrid,Spain;Department F.-A.Forel for Environmental and Aquatic Sciences,University of Geneva,Geneva CH-1205,Switzerland
机标中图分类号,由域田数据科技根据网络公开资料自动分析生成,仅供学习研究参考。