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中国科学院植物研究所机构知识库
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面向高精度城市测绘的激光紧耦合SLAM方法
期刊论文
测绘学报, 2021, 卷号: 50, 期号: 11, 页码: 1585-1593
Authors:
孙喜亮
;
关宏灿
;
苏艳军
;
徐光彩
;
郭庆华
Adobe PDF(10258Kb)
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Submit date:2022/04/24
同时定位与地图构建
紧耦合
特征提取
点云配准
全局优化
基于激光雷达的自然资源三维动态监测现状与展望
期刊论文
遥感学报, 2021, 卷号: 25, 期号: 01, 页码: 381-402
Authors:
李玉美
;
郭庆华
;
万波
;
秦宏楠
;
王德智
;
徐可心
;
宋师琳
;
孙千惠
;
赵晓霞
;
杨默含
;
吴晓永
;
魏邓杰
;
胡天宇
;
苏艳军
Adobe PDF(1192Kb)
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遥感
激光雷达
自然资源
三维信息
动态监测
数据融合
中国植被生态学研究的进展和趋势
期刊论文
中国科学:生命科学, 2021, 卷号: 51, 期号: 03, 页码: 215-218
Authors:
马克平
;
郭庆华
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植被生态学
植物群落
新一代
植被图
植被制图
轻小型无人机遥感及其行业应用进展
期刊论文
地理科学进展, 2021, 卷号: 40, 期号: 09, 页码: 1550-1569
Authors:
郭庆华
;
胡天宇
;
刘瑾
;
金时超
;
肖青
;
杨贵军
;
高显连
;
许强
;
谢品华
;
彭炽刚
;
闫利
Adobe PDF(694Kb)
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数据融合
多传感器集成
智能化飞行
无人机组网
深度学习
“绿途”系统:公民科学时代的植被调查制图新工具
期刊论文
中国科学:生命科学, 2021, 卷号: 51, 期号: 03, 页码: 362-374
Authors:
金时超
;
胡天宇
;
苏艳军
;
马勤
;
关宏灿
;
杨默含
;
郭庆华
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绿途
植被图
众包
公民科学
APP
网页端
新一代1:50万中国植被图绘制方法探讨
期刊论文
中国科学:生命科学, 2021, 卷号: 51, 期号: 03, 页码: 229-241
Authors:
郭庆华
;
关宏灿
;
胡天宇
;
金时超
;
苏艳军
;
王雪静
;
魏邓杰
;
马勤
;
孙千惠
Adobe PDF(10609Kb)
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中国植被图
植被制图
卫星遥感
众源采集
深度学习
An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images
期刊论文
SENSORS, 2020, 卷号: 20, 期号: 22
Authors:
Sun, Fei
;
Fang, Fang
;
Wang, Run
;
Wan, Bo
;
Guo, Qinghua
;
Li, Hong
;
Wu, Xincai
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image classification
class imbalance
impartial semi-supervised learning strategy (ISS)
extreme gradient boosting (XGB)
very-high-resolution (VHR)
Drought-modulated allometric patterns of trees in semi-arid forests
期刊论文
COMMUNICATIONS BIOLOGY, 2020, 卷号: 3, 期号: 1
Authors:
Dai, Jingyu
;
Liu, Hongyan
;
Wang, Yongcai
;
Guo, Qinghua
;
Hu, Tianyu
;
Quine, Timothy
;
Green, Sophie
;
Hartmann, Henrik
;
Xu, Chongyang
;
Liu, Xu
;
Jiang, Zihan
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Separating the Structural Components of Maize for Field Phenotyping Using Terrestrial LiDAR Data and Deep Convolutional Neural Networks
期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 卷号: 58, 期号: 4, 页码: 2644-2658
Authors:
Jin, Shichao
;
Su, Yanjun
;
Gao, Shang
;
Wu, Fangfang
;
Ma, Qin
;
Xu, Kexin
;
Hu, Tianyu
;
Liu, Jin
;
Pang, Shuxin
;
Guan, Hongcan
;
Zhang, Jing
;
Guo, Qinghua
Adobe PDF(14928Kb)
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Classification
deep learning
LiDAR
phenotype
segmentation
structural components
Application of deep learning in ecological resource research: Theories, methods, and challenges
期刊论文
SCIENCE CHINA-EARTH SCIENCES, 2020, 卷号: 63, 期号: 10, 页码: 1457-1474
Authors:
Guo, Qinghua
;
Jin, Shichao
;
Li, Min
;
Yang, Qiuli
;
Xu, Kexin
;
Ju, Yuanzhen
;
Zhang, Jing
;
Xuan, Jing
;
Liu, Jin
;
Su, Yanjun
;
Xu, Qiang
;
Liu, Yu
Adobe PDF(4690Kb)
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Ecological resources
Deep learning
Neural network
Big data
Theory and tools
Application and challenge