[1]温昱婷,赵静*,兰玉彬,等. 基于无人机可见光影像和面向对象的树种分类研究[J].西北林学院学报,2022,37(1):74-80.[doi:10.3969/j.issn.1001-7461.2022.01.11]
　WEN Yu-ting,ZHAO Jing*,LAN Yu-bin,et al. Classifications of Tree Species Based on UAV’s Visible Light Images and Object-Oriented Method[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2022,37(1):74-80.[doi:10.3969/j.issn.1001-7461.2022.01.11]
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 基于无人机可见光影像和面向对象的树种分类研究()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第37卷
期数:
2022年第1期
页码:
74-80
栏目:
出版日期:
2022-01-31

文章信息/Info

Title:
 Classifications of Tree Species Based on UAV’s Visible Light Images and Object-Oriented Method
文章编号:
1001-7461(2022)01-0074-07
作者:
 温昱婷1; 2; 赵静1; 2*; 兰玉彬1; 2; 杨东建1; 2; 潘方江1; 2; 曹佃龙1; 2
 （1.山东理工大学 农业工程与食品科学学院，山东 淄博 255000；2.山东理工大学 生态无人农场研究院，山东 淄博 255000）
Author(s):
 WEN Yu-ting1; 2; ZHAO Jing1; 2*; LAN Yu-bin1; 2; YANG Dong-jian1; 2; PAN Fang-jiang1; 2; CAO Dian-long1; 2
 (1.School of Agricultural Engineering and Food Science,Shandong University of Technology,Zibo 255000,Shandong,China; 2.Research Institute of Ecological Unmanned Farm,Shandong University of Technology,Zibo 255000,Shandong,China)
关键词:
 ; 无人机遥感; 可见光影像; 面向对象; 数字表面模型
Keywords:
 ; drone remote sensing;  visible light image;  object-oriented;  digital surface model
分类号:
S771.8
DOI:
10.3969/j.issn.1001-7461.2022.01.11
文献标志码:
A
摘要:
 针对目前林业部门人工调查树种存在效率低、成本高等问题，采用无人机遥感技术进行树种分类识别，提高树种调查效率，辅助林业管理部门进行林木种植结构分析、病虫害防治等工作。利用无人机获取矮冬青、三球悬铃木、马尾松和紫叶李的冠层红绿蓝（red-green-blue,RGB）可见光影像，进行数字表面模型（digital surface model，DSM）特征图像提取，通过色彩空间转换提高树种间颜色差异；应用最优尺度分割，以纹理特征、颜色特征及几何特征为分类特征参数，优选最佳分类特征集，以期实现无人机可见光影像的树种分类。结果表明，DSM与RGB特征融合图像提取树种的精度较高，可见光影像分类总精度为91.58%，Kappa系数为0.89；特征融合图像分类总精度为98.27%，Kappa系数为0.98。研究提出的特征融合图像结合面向对象分类方法实现了可见光影像的树种分类，为实现树种计数、统计、分类提供数据参考。
Abstract:
 Aiming at solving the problems of low efficiency and high cost in the artificial investigation of tree species in the forestry sector,the remote sensing technology of unmanned aerial vehicle (UAV) was used to classify and identify tree species to improve the investigation efficiency and to assist the forest management department in the analysis of tree planting structure,pest control,etc.Red-green-blue (RGB) visible light images of the canopy of Ilex lohfauensis,Platanus orientalis,Pinus massoniana,and Prunus cerasifera were obtained by UAV,and feature images of digital surface model (DSM) were extracted,the color differences among tree species were improved by color space conversion.The best classification feature set was optimized by using optimal scale segmentation and taking texture feature,color feature and geometric feature as classification feature parameters to realize the classification of tree species in UAV visible light images.The results showed that the accuracy of extracting tree species from DSM and RGB feature fusion images was high.The classification accuracy of the visible image achieved 91.58%,and the Kappa coefficient was 0.89; the total accuracy of feature fusion image classification was 98.27%,and the Kappa coefficient was 0.98.The feature fusion image was combined with the object-oriented classification method to realize the classification of tree species in the visible light image,which provided data reference for tree species counting,statistics and classification.

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备注/Memo

备注/Memo:
 收稿日期：2021-02-23修回日期：2021-08-19
基金项目：山东省引进顶尖人才“一事一议”专项经费（鲁政办字［2018］27号）;淄博市重点研发计划（校城融合类）生态无人农场研究院项目（2019ZBXC200）;山东省农业重大应用技术创新项目(SD2019ZZ019)。
第一作者：温昱婷。研究方向：无人机遥感技术与应用。E-mail:1256596123@qq.com
*通信作者：赵静，副教授，博士。研究方向：农业遥感技术与智能检测。E-mail:zbceozj@163.com
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