城市植被遥感分类研究进展与展望  

Research progress and prospects of remote sensing classification of urban vegetation

作　　者：孟庆岩 杜弘宇 王莉萍 张琳琳 吴嘉豪 康佳琦 MENG Qingyan;DU Hongyu;WANG Liping;ZHANG Linlin;WU Jiahao;KANG Jiaqi(Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;Center for Urban Governance Studies of Zhejiang Province,Hangzhou International Urbanology Research Center,Hangzhou 310000,Zhejiang,China;Key Laboratory of Earth Observation of Hainan Province,Hainan Aerospace Information Research Institute,Sanya 572029,Hainan,China;University of Chinese Academy of Sciences,Beijing 100049,China;State Key Laboratory of Internet of Things for Smart City,University of Macao,Macao 999078,China;School of Life and Environmental Sciences,Guilin University of Electronic Technology,Guilin 541004,Guangxi,China)

机构地区：[1]中国科学院空天信息创新研究院,北京100094 [2]杭州国际城市学研究中心浙江省城市治理研究中心,浙江杭州310000 [3]海南空天信息研究院,海南省地球观测重点实验室,海南三亚572029 [4]中国科学院大学,北京100049 [5]澳门大学智慧城市物联网国家重点实验室,中国澳门999078 [6]桂林电子科技大学生命与环境科学学院,广西桂林541004

出　　处：《浙江大学学报（农业与生命科学版）》2024年第2期190-199,共10页Journal of Zhejiang University:Agriculture and Life Sciences

基　　金：海南省自然科学基金项目(423CXTD390);国家自然科学基金面上项目(42171357);风云三号03批气象卫星工程地面应用系统生态监测评估应用项目(第一期)(ZQC-R22227);中国科学院青年创新促进会项目(2023139)。

摘　　要：城市植被是城市环境的重要组成部分,城市植被遥感分类是对城市绿度空间监测分析的重要方式。本文通过梳理国内外城市植被遥感分类研究进展,从遥感数据源和分类方法入手,分析该领域目前面临的问题及发展趋势,以期为城市绿度空间研究提供参考。首先,概述了光学数据、激光雷达数据及地面传感数据等数据源在城市植被遥感分类领域的应用,对不同数据源的优势与不足进行了深入分析;其次,基于阈值分割、机器学习和深度学习3种分类方法的研究,总结了应用于城市植被遥感分类领域各方法的特点;最后,提出了城市植被遥感分类研究中现存问题和未来发展方向。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.

关 键 词：城市植被 城市遥感 图像分类 

分 类 号：P237[天文地球—摄影测量与遥感]