摘要
在介绍了无人机平台及携带的多种传感器的基础上,总结了无人机遥感提取植被覆盖度的算法,并将算法分成了三大类:颜色空间法、植被指数法和机器学习分类法;针对无人机遥感提取植被覆盖度所存在的问题及发展趋势进行了讨论.结果表明:颜色空间法能有效消除无人机图像中亮度和饱和度等因素的干扰;植被指数法原理简单,且具有良好的精度,是应用最广泛的方法;机器学习分类法适用于不同传感器获取的图像且提取精度高.
Based on the introduction of the UAV platforms and various sensors,this paper mainly summarizes the method used to extract fractional vegetation cover from UAV remote sensing data,which are divided into three categories:color space method,vegetation index method and machine learning classification.The issues and challenges of the retrieval of fractional vegetation cover from UAV are discussed.The results show that color space method can eliminate the influence of brightness,saturation and other factors in UAV images.Vegetation index method is most widely used at present which is simple in principle and has good accuracy.Machine learning classification method is suitable for images acquired by various sensors and has relatively high accuracy in vegetation extraction.
作者
刘琳
郑兴明
姜涛
李雷
丁艳玲
LIU Lin;ZHENG Xing-ming;JIANG Tao;LI Lei;DING Yan-ling(School of Geographical Sciences,Northeast Normal University,Changchun 130024,China;Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains,Ministry of Education,Northeast Normal University,Changchun 130024,China;Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102,China)
出处
《东北师大学报（自然科学版）》
CAS
北大核心
2021年第4期151-160,共10页
Journal of Northeast Normal University(Natural Science Edition)
基金
中央高校基本科研业务费专项资金资助项目(2412020FZ004)
吉林省科技发展计划项目(20180201012GX,20180623058).
关键词
无人机
遥感
植被覆盖度
提取算法
unmanned aerial vehicle
remote sensing
fractional vegetation cover
extraction algorithm
