基于室内高光谱激光雷达的典型树种叶片光谱观测和分类
投稿时间:2019-11-13  修订日期:2020-03-20  点此下载全文
引用本文:胡佩纶,陈育伟,蒋长辉,林起楠,李伟,漆建波,俞琳锋,邵慧,黄华国.基于室内高光谱激光雷达的典型树种叶片光谱观测和分类[J].红外与毫米波学报,2020,39(3):372~380].HU Pei-Lun,CHEN Yu-Wei,JIANG Chang-Hui,LIN Qi-Nan,LI Wei,QI Jian-Bo,YU Lin-Feng,Shao Hui,HUANG Hua-Guo.Spectral observation and classification of typical tree species leaves based on indoor hyperspectral lidar[J].J.Infrared Millim.Waves,2020,39(3):372~380.]
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作者单位E-mail
胡佩纶 <
institution>
北京林业大学林学院省部共建森林培育与保护教育部重点实验室 
hupeilun_818@163.com 
陈育伟 芬兰地理空间研究所遥感和摄影测量部基尔科努米 Yuwei.chen@nls.fi 
蒋长辉 <
institution>
芬兰地理空间研究所遥感和摄影测量部基尔科努米 
changhui.jiang@njust.edu.cn 
林起楠 <
institution>
北京林业大学林学院省部共建森林培育与保护教育部重点实验室 
qinan_lin2017@bjfu.edu.cn 
李伟 中国科学院光电研究院定量遥感信息技术重点实验室 liwei@aoe.ac.cn 
漆建波 北京林业大学林学院省部共建森林培育与保护教育部重点实验室 jianboqi@bjfu.edu.cn 
俞琳锋 <
institution>
北京林业大学林学院省部共建森林培育与保护教育部重点实验室 
ylf1801@126.com 
邵慧 安徽建筑大学电子与信息工程学院 shaohui@ahjzu.edu.cn 
黄华国 <
institution>
北京林业大学林学院省部共建森林培育与保护教育部重点实验室 
huaguo_huang@bjfu.edu.cn 
基金项目:国家重点研发计划项目 2017YFC0504003-4;国家自然科学基金 41571332国家重点研发计划项目(2017YFC0504003-4);国家自然科学基金(41571332)
中文摘要:高光谱激光雷达综合了高光谱和激光雷达特征,可为植被生理生化参数提取提供更加精确的遥感探测,但其应用潜力尚未得到充分挖掘。以北京10个典型树种的单叶为样本,开展室内高光谱激光雷达的叶片观测试验,并进行树种分类研究,为未来高光谱激光雷达的林业应用提供基础。首先进行可调谐高光谱激光雷达(Hyperspectral LiDAR,HSL)叶片高光谱测量,并完成与ASD地物光谱仪所测数据对比实验;其次,应用随机森林方法实现10种叶片的分类研究,其输入的特征指数为融合全部波段、部分敏感波段的光谱指数。结果表明:(a)HSL在波段650~1 000 nm(71个通道)内观测的叶片高光谱和ASD光谱一致(R2=0.9525-0.9932,RMSE=0.0587);(b)只用原始波段反射率分类精度为78.31%,其中分类贡献率最大波段的是650-750 nm,使用此波段进行分类精度为94.18%,表明利用红边波段(650~750nm)进行树种分类是十分有效的;(c)对树种敏感的波段为680 nm、685 nm、690 nm、715 nm、720 nm、725 nm、730 nm;(d)结合敏感波段光谱指数与植被指数分类精度82.65%。该研究结果表明在单叶级别,利用高光谱激光雷达能够准确地反映目标叶片的光谱特征并且能有效进行树种分类;未来将可能在野外应用中精确提取目标的生理生化参数。
中文关键词:高光谱激光雷达  叶片分类  全波形  植被参数
 
Spectral observation and classification of typical tree species leaves based on indoor hyperspectral lidar
Abstract:Hyperspectral laser radar combines the characteristics of LiDAR and hyperspectral information, and provides more accurate remote sensing detection methods of the extraction of vegetation physiological and biochemical parameters, but its application potential has not been fully explored. In this paper, the leaves of 10 typical tree species in Beijing are taken as samples to carry out the leaf observation experiment of indoor hyperspectral laser radar. And the tree species classification research is carried out to provide the basis of the future forestry application of hyperspectral laser radar. In this study, the hyperspectral data of tunable hyperspectral LiDAR (HSL) was carried out and compared with the data measured by ASD spectrometer. Secondly, 10 kinds of leaves were classified by random forest method. In the process, the total spectral index is obtained by combining all the bands and some sensitive bands with the spectral index. The results show that: (a) HSL is consistent with ASD spectra observed in the band 650~1 000 nm (71 channels) (R2=0.9525~0.993 2, RMSE=0.058 7); (b) The classification accuracy of the original band reflectivity is 78.31%, there into the maximum contribution rate of the classification band is 650~750 nm, and the classification accuracy is 94.18% using this band which shows that it is very effective in classify tree species by using red edge band (650~750 nm); (c) the bands sensitive to tree species classification are 680 nm, 685 nm, 690 nm, 715 nm, 720 nm, 725 nm, 730 nm; d) When we combine the spectral index and vegetation index, the classification accuracy is 82.65%. This study shows that at the single leaf level, hyperspectral LiDAR can accurately reflect the spectral characteristics of the target leaves and classify the species of different trees effectively. It is possible to extract physiological and biochemical parameters of targets for the future field applications accurately.
keywords:Hyperspectral LiDAR  leaf classification  full bands  vegetation index
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