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.

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。