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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  
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
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Ecological resources  Deep learning  Neural network  Big data  Theory and tools  Application and challenge  
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
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Biomass  Phenotype  Machine learning  Terrestrial lidar  Precision agriculture  
A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments 期刊论文
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2020, 卷号: 13, 页码: 3958-3974
Authors:  Jin, Shichao;  Sun, Yanjun;  Zhao, Xiaoqian;  Hu, Tianyu;  Guo, Qinghua
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Digital terrain model (DTM)  deep learning  fully convolutional neural network (FCN)  ground filtering  light detection and ranging (LiDAR)  
Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data 期刊论文
ECOLOGICAL INDICATORS, 2020, 卷号: 108
Authors:  Xu, Kexin;  Su, Yanjun;  Liu, Jin;  Hu, Tianyu;  Jin, Shichao;  Ma, Qin;  Zhai, Qiuping;  Wang, Rui;  Zhang, Jing;  Li, Yumei;  Liu, Hon An;  Guo, Qinghua
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Aboveground biomass (AGB)  Degraded grassland  Machine learning  Northern agro-pastoral ecotone  Terrestrial laser scanning (TLS)  
An Ensemble of Classifiers Based on Positive and Unlabeled Data in One-Class Remote Sensing Classification 期刊论文
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2018, 卷号: 11, 期号: 2, 页码: 572-584
Authors:  Liu, Ran;  Li, Wenkai;  Liu, Xiaoping;  Lu, Xingcheng;  Li, Tianhong;  Guo, Qinghua
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Classifier ensemble  one-class classification  positive and unlabeled learning (PUL)  weighted average  weighted vote  
No significant changes in topsoil carbon in the grasslands of northern China between the 1980s and 2000s 期刊论文
SCIENCE OF THE TOTAL ENVIRONMENT, 2018, 卷号: 624, 页码: 1478-1487
Authors:  Liu, Shangshi;  Yang, Yuanhe;  Shen, Haihua;  Hu, Huifeng;  Zhao, Xia;  Li, He;  Liu, Taoyu;  Fang, Jingyun
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Alpine grasslands  Artificial neural network  Climate change  Random forests  Soil organic carbon  Temperate grasslands  
Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms 期刊论文
FRONTIERS IN PLANT SCIENCE, 2018, 卷号: 9
Authors:  Jin, Shichao;  Su, Yanjun;  Gao, Shang;  Wu, Fangfang;  Hu, Tianyu;  Liu, Jin;  Li, Wankai;  Wang, Dingchang;  Chen, Shaojiang;  Jiang, Yuanxi;  Pang, Shuxin;  Guo, Qinghua
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deep learning  detection  classification  segmentation  phenotype  Lidar (light detection and ranging)  
One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm 期刊论文
REMOTE SENSING, 2017, 卷号: 9, 期号: 10
Authors:  Ao, Zurui;  Su, Yanjun;  Li, Wenkai;  Guo, Qinghua;  Zhang, Jing
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LiDAR  one-class classification  presence and background learning algorithm  remote sensing  
Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data 期刊论文
REMOTE SENSING, 2017, 卷号: 9, 期号: 8
Authors:  Wang, Run;  Wan, Bo;  Guo, Qinghua;  Hu, Maosheng;  Zhou, Shunping
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urban mapping  one-class  NPP-VIIRS DNB  MODIS NDVI  large scale