[1]孙英伟,林文树*. 基于SFM算法的单木结构参数提取研究[J].西北林学院学报,2020,35(5):180-184.[doi:10.3969/j.issn.1001-7461.2020.05.28]
　SUN Ying-wei,LIN Wen-shu*. Extraction of the Parameters of Single Tree Structure Based on SFM Algorithm[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2020,35(5):180-184.[doi:10.3969/j.issn.1001-7461.2020.05.28]
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 基于SFM算法的单木结构参数提取研究()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第35卷
期数:
2020年第5期
页码:
180-184
栏目:
出版日期:
2020-09-30

文章信息/Info

Title:
 Extraction of the Parameters of Single Tree Structure Based on SFM Algorithm
文章编号:
1001-7461(2020)05-0180-05
作者:
 孙英伟; 林文树*
 （东北林业大学 工程技术学院，黑龙江 哈尔滨 150040）
Author(s):
 SUN Ying-wei; LIN Wen-shu*
 (College of Engineering and Technology,Northeast Forestry University,Harbin 150040,Heilongjiang,China)
关键词:
 ; SFM算法; 特征点提取; 点云; 单木结构参数
Keywords:
 ; SFM algorithm;  feature point extraction;  point cloud;  individual tree structure parameter
分类号:
S758.1
DOI:
10.3969/j.issn.1001-7461.2020.05.28
文献标志码:
A
摘要:
 针对传统森林资源调查方法获取单木结构参数效率低和成本高的问题，提出一种基于SFM算法的单木结构参数快速提取方法。以哈尔滨市城市林业示范基地树木为研究对象，利用SFM算法获得单木照片的三维点云，并利用点云数据处理软件对获得的点云数据进行单木结构参数提取，最后与实测参数进行对比分析。结果表明：1）分别利用SIFT算法、SURF算法以及ORB算法对相机校检后的树木照片进行特征点提取匹配，特征点正确匹配个数分别为23、145以及25，相应的耗时分别为18.56、16.04、1.58 s；2）利用SFM算法能获得树木照片的稀疏点云和稠密点云，平均每棵树木点云量为80万个；3）基于点云数据提取单木结构参数的胸径、树高及冠幅的平均绝对误差分别为1.79 cm、0.77 m及0.79 m；胸径、树高、冠幅的提取值与实测值相关系数均>0.94。
Abstract:
 Aiming at solving the problems of low efficiency and high cost of single tree structure parameter extraction based on the traditional forest resource survey method,a fast extraction method based on structure from motion (SFM) algorithm was proposed.Trees in Harbin Urban Forestry Demonstration Base were used as the research objects,3D point clouds of single tree photos using SFM algorithm were obtained.The point cloud data processing software was used to extract the single tree structure parameters from the obtained point cloud data,and which were finally compared with the measured parameters.The results showed that 1) three algorithms,i.e.,SIFT,SURF and ORB were used to extract and match the feature points on the camera calibrated tree photos,respectively.The numbers of feature points matching of the three algorithms were 23,145 and 25,respectively.The corresponding time consumptions were 18.56,16.04 and 1.58 s respectively.2) The sparse point clouds and dense point clouds of the tree photos could be obtained by using SFM algorithm,and the average amount of cloud data per tree was 800 000.3) The average absolute errors of DBH,tree height and tree crown of the single tree structure parameters based on point cloud data were 1.79 cm,0.77 m and 0.79 m,respectively; The correlation coefficient between the extracted values of the DBH,tree height and crown width and the measured values were all above 0.94.

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备注/Memo

备注/Memo:
 收稿日期：2019-08-31修回日期：2020-01-12
基金项目：中央高校基本科研业务费专项资金（2572019BL03）；国家自然科学基金（31971574）；黑龙江省博士后基金（LBH-Z15007）。
作者简介：孙英伟。研究方向：林业遥感。E-mail：bluesun5474@qq.com
*通信作者：林文树，教授。研究方向：森林资源遥感监测。E-mail：linwenshu@nefu.edu.cn
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