摘要
针对单木识别研究中"局部最大值"算法因窗口大小设置不同而产生的单木漏识别与错识别问题,提出了联合"局部最大值"与"单木树冠结构分析"的单木识别算法。算法首先利用"局部最大值"获得候选单木;然后对候选单木树冠结构进行分析,提取树冠结构曲线;最后通过对树冠结构曲线判别,剔除、合并错识别与过识别单木,从而提高单木识别的精度。选取大兴安岭林区8个典型样地进行实验,以实测可见单木为参考,与窗口大小为1.0m、2.0m的两种"局部最大值"算法进行比较。结果表明,该算法8个样地整体F测度为90.45%,相比1.0m、2.0m窗口的"局部最大值"法F测度74.82%、77.35%,分别提高了15.63%、13.10%。
The traditional'local maximum'algorithm with different settings of window size could lead to the error and missing of individual tree recognition.This paper presents an algorithm combination of'local maximum'and 'tree crown structure analysis'based on image matching point cloud.The algorithm obtained the candidate trees by'local maximum'and then got the crown structure curve by the analysis of the candidate tree.The false and over recognition trees were eliminated and merged by the crown structure curve.The precision of the algorithm was verified by 8 plots in Daxing’an mountains forest region.The visible trees were used as the reference.The results were compared with the results of the traditional local maxima algorithm with the window size by 1.0 mand 2.0 m.Results showed that F-Score of the proposed algorithm was 90.45%,which increased by 15.63%and13.10%from 74.82%and 77.35% when compared with the traditional local maxima algorithm with the window size by 1 mand 2 m,respectively.
作者
刘见礼
张志玉
倪文俭
汪垚
张大凤
LIU Jianli;ZHANG Zhiyu;NI Wenjian;WANG Yao;ZHANG Dafeng(State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing100101,China;University of Chinese Academy of Science,Beijing100049,China)
出处
《遥感信息》
CSCD
北大核心
2019年第1期93-101,共9页
Remote Sensing Information
基金
国家973计划(2013CB733404
2013CB733401)
国家自然科学基金(41301395
41471311)
关键词
无人机
影像匹配点云
树冠结构
单木识别
局部最大值
UAV
image matching point cloud
crown structure
individual tree recognition
local maximum
