Research progress and prospects of remote sensing classification of urban vegetation
Urban vegetation is an important part of the urban environment,and remote sensing classification of urban vegetation is an important way to monitor and analyze urban green space.By sorting the research progress of remote sensing classification of urban vegetation at home and abroad,we started from two aspects of remote sensing data sources and classification methods,and analyzed the current problems and development trends in this field,in order to provide references for urban green space research.First,the applications of optical data,light detection and ranging(LiDAR)data and ground sensing data in the remote sensing classification of urban vegetation were summarized,and the advantages and disadvantages of different data sources were analyzed in depth.Second,the characteristics of classification methods applied in the remote sensing classification of urban vegetation were summarized through the study of three classification methods,including threshold segmentation,machine learning,and deep learning.Finally,the existing problems and future development directions in the remote sensing classification of urban vegetation were proposed.
NETL
NSTL
万方数据
