Unsupervised Semantic Segmenting TLS Data of Individual Tree Based on Smoothness Constraint Using Open-Source Datasets

点云 分割 计算机科学 树(集合论) 图像分割 人工智能 模式识别(心理学) 遥感 数学 地质学 数学分析
作者
Yanqi Dong,Zhibin Ma,Fang Xu,Feixiang Chen
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:60: 1-15 被引量:1
标识
DOI:10.1109/tgrs.2022.3218442
摘要

Unsupervised segmentation of Terrestrial Laser Scanning (TLS) data into wood and leaf is the key for studying forest carbon storage, photosynthesis, canopy radiation. Further segmentation of wood data into trunk and larger branch (TLB), remaining branch (RB) is of great significance and challenge for dust retention, soil heavy metal enrichment. We proposed an unsupervised, automatic semantic segmentation method based on TLS data of individual tree. The method firstly performs initial segmentation based on plane fitting residuals and neighborhood normal angle, which can extract smooth and connected regions in point cloud. Then, the geometric features of segmented clusters are quantified to approximate RB or leaf features. Finally, the segmentation of TLB, RB, and leaf is realized by combining different clusters from bottom to top with geometric features and neighborhood relations. The segmentation performance of our method was evaluated with 104 tree samples from 23 tree species in two open-source datasets from Indonesia, Peru, Guyana and from Canada and Finland. The micro-average precision of our method is 93.61%. The micro-average recalls of TLB, RB, and leaf are 97.08%, 86.44%, and 89.62%. Compared with the well-known method of separating wood and leaf, our method has 33.56% higher sensitivity, 1.82% higher specificity, 20.52% higher precision, and 0.217 higher F1-score. Besides, we estimated the surface area and volume of TLB, the surface area and volume of RB based on the segmented data. The above parameters have good consistency compared to those calculated based on manually separated point clouds (Pearson correlation coefficient (PCC) of 0.55-0.93).
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
大大大大管子完成签到 ,获得积分10
刚刚
刚刚
Hello应助祎思采纳,获得10
1秒前
华仔应助zunzun采纳,获得10
1秒前
影子发布了新的文献求助10
1秒前
iNk应助兴奋大开采纳,获得10
2秒前
3秒前
小帕菜完成签到,获得积分10
3秒前
花生完成签到 ,获得积分10
3秒前
3秒前
泡泡完成签到 ,获得积分10
4秒前
小马甲应助百汇科研采纳,获得10
5秒前
何果果完成签到,获得积分10
5秒前
老李完成签到,获得积分10
6秒前
6秒前
6秒前
科目三应助晚风采纳,获得50
7秒前
糕糕发布了新的文献求助10
8秒前
麦子完成签到 ,获得积分10
8秒前
She完成签到,获得积分10
8秒前
灵梦柠檬酸完成签到,获得积分10
8秒前
8秒前
沉默大白关注了科研通微信公众号
9秒前
9秒前
wufel完成签到,获得积分10
9秒前
liu完成签到 ,获得积分10
10秒前
驰驰发布了新的文献求助10
10秒前
yy完成签到 ,获得积分10
11秒前
jia完成签到 ,获得积分20
12秒前
Alrigh-t完成签到,获得积分10
13秒前
李小鑫吖发布了新的文献求助10
13秒前
可爱的函函应助入戏太深采纳,获得10
13秒前
CodeCraft应助竹喧私语采纳,获得10
14秒前
replica完成签到,获得积分10
14秒前
山林完成签到,获得积分10
14秒前
14秒前
完美世界应助希勤采纳,获得10
15秒前
可耐的白山完成签到,获得积分10
15秒前
ark861023发布了新的文献求助10
15秒前
fzhou完成签到 ,获得积分10
16秒前
高分求助中
좌파는 어떻게 좌파가 됐나:한국 급진노동운동의 형성과 궤적 2500
Sustainability in Tides Chemistry 1500
TM 5-855-1(Fundamentals of protective design for conventional weapons) 1000
Cognitive linguistics critical concepts in linguistics 800
Threaded Harmony: A Sustainable Approach to Fashion 799
Livre et militantisme : La Cité éditeur 1958-1967 500
氟盐冷却高温堆非能动余热排出性能及安全分析研究 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3052912
求助须知:如何正确求助?哪些是违规求助? 2710137
关于积分的说明 7419790
捐赠科研通 2354754
什么是DOI,文献DOI怎么找? 1246249
科研通“疑难数据库(出版商)”最低求助积分说明 606002
版权声明 595975