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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
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Classification  deep learning  LiDAR  phenotype  segmentation  structural components  
An updated Vegetation Map of China (1:1000000) 期刊论文
SCIENCE BULLETIN, 2020, 卷号: 65, 期号: 13, 页码: 1125-1136
Authors:  Su, Yanjun;  Guo, Qinghua;  Hu, Tianyu;  Guan, Hongcan;  Jin, Shichao;  An, Shazhou;  Chen, Xuelin;  Guo, Ke;  Hao, Zhanqing;  Hu, Yuanman;  Huang, Yongmei;  Jiang, Mingxi;  Li, Jiaxiang;  Li, Zhenji;  Li, Xiankun;  Li, Xiaowei;  Liang, Cunzhu;  Liu, Renlin;  Liu, Qing;  Ni, Hongwei;  Peng, Shaolin;  Shen, Zehao;  Tang, Zhiyao;  Tian, Xingjun;  Wang, Xihua;  Wang, Renqing;  Xie, Zongqiang;  Xie, Yingzhong;  Xu, Xiaoniu;  Yang, Xiaobo;  Yang, Yongchuan;  Yu, Lifei;  Yue, Ming;  Zhang, Feng;  Ma, Keping
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Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level 期刊论文
PLANT METHODS, 2020, 卷号: 16, 期号: 1
Authors:  Jin, Shichao;  Su, Yanjun;  Song, Shilin;  Xu, Kexin;  Hu, Tianyu;  Yang, Qiuli;  Wu, Fangfang;  Xu, Guangcai;  Ma, Qin;  Guan, Hongcan;  Pang, Shuxin;  Li, Yumei;  Guo, Qinghua
Adobe PDF(4774Kb)  |  Favorite  |  View/Download：0/0  |  Submit date：2022/01/06
Biomass  Phenotype  Machine learning  Terrestrial lidar  Precision agriculture  
A marker-free method for registering multi-scan terrestrial laser scanning data in forest environments 期刊论文
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2020, 卷号: 166, 页码: 82-94
Authors:  Guan, Hongcan;  Su, Yanjun;  Sun, Xiliang;  Xu, Guangcai;  Li, Wenkai;  Ma, Qin;  Wu, Xiaoyong;  Wu, Jin;  Liu, Lingli;  Guo, Qinghua
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Terrestrial laser scanning  Registration  Marker-free  Forest  
An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications 期刊论文
REMOTE SENSING, 2019, 卷号: 11, 期号: 24
Authors:  Guan, Hongcan;  Su, Yanjun;  Hu, Tianyu;  Chen, Jin;  Guo, Qinghua
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spatiotemporal data fusion  object-based framework  similar pixel  vegetation mapping