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Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing 期刊论文
CROP JOURNAL, 2022, 卷号: 10, 期号: 5, 页码: 1334-1345
作者:  Li, Qing;  Jin, Shichao;  Zang, Jingrong;  Wang, Xiao;  Sun, Zhuangzhuang;  Li, Ziyu;  Xu, Shan;  Ma, Qin;  Su, Yanjun;  Guo, Qinghua;  Jiang, Dong
Adobe PDF(3253Kb)  |  收藏  |  浏览/下载：8/0  |  提交时间：2024/03/07
LiDAR  Multispectral  Yield  Phenotype  Hyper-temporal  
Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data 期刊论文
REMOTE SENSING OF ENVIRONMENT, 2022, 卷号: 269
作者:  Liu, Xiaoqiang;  Su, Yanjun;  Hu, Tianyu;  Yang, Qiuli;  Liu, Bingbing;  Deng, Yufei;  Tang, Hao;  Tang, Zhiyao;  Fang, Jingyun;  Guo, Qinghua
Adobe PDF(6152Kb)  |  收藏  |  浏览/下载：33/0  |  提交时间：2024/03/07
Forest canopy height  GEDI  ICESat-2 ATLAS  Lidar  Spatial interpolation  Deep neural network  
One-Class Remote Sensing Classification From Positive and Unlabeled Background Data 期刊论文
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 卷号: 14, 页码: 730-746
作者:  Li, Wenkai;  Guo, Qinghua;  Elkan, Charles
Adobe PDF(11598Kb)  |  收藏  |  浏览/下载：72/0  |  提交时间：2023/02/24
Training  Remote sensing  Classification algorithms  Mathematical model  Support vector machines  Data models  Prediction algorithms  Case-control sampling  labeled and unlabeled data  one-class classification  positive and background learning with constraints (PBLC)  remote sensing  
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
作者:  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)  |  收藏  |  浏览/下载：130/0  |  提交时间：2022/03/01
Classification  deep learning  LiDAR  phenotype  segmentation  structural components  
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
作者:  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)  |  收藏  |  浏览/下载：89/0  |  提交时间：2022/03/01
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
作者:  Jin, Shichao;  Sun, Yanjun;  Zhao, Xiaoqian;  Hu, Tianyu;  Guo, Qinghua
Adobe PDF(7633Kb)  |  收藏  |  浏览/下载：84/0  |  提交时间：2022/03/01
Digital terrain model (DTM)  deep learning  fully convolutional neural network (FCN)  ground filtering  light detection and ranging (LiDAR)  
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
作者:  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)  |  收藏  |  浏览/下载：102/0  |  提交时间：2022/03/01
Ecological resources  Deep learning  Neural network  Big data  Theory and tools  Application and challenge  
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
作者:  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
Adobe PDF(4794Kb)  |  收藏  |  浏览/下载：93/0  |  提交时间：2022/03/01
Aboveground biomass (AGB)  Degraded grassland  Machine learning  Northern agro-pastoral ecotone  Terrestrial laser scanning (TLS)  
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
作者:  Jin, Shichao;  Su, Yanjun;  Gao, Shang;  Wu, Fangfang;  Hu, Tianyu;  Liu, Jin;  Li, Wankai;  Wang, Dingchang;  Chen, Shaojiang;  Jiang, Yuanxi;  Pang, Shuxin;  Guo, Qinghua
Adobe PDF(2278Kb)  |  收藏  |  浏览/下载：77/0  |  提交时间：2022/02/25
deep learning  detection  classification  segmentation  phenotype  Lidar (light detection and ranging)  
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
作者:  Liu, Shangshi;  Yang, Yuanhe;  Shen, Haihua;  Hu, Huifeng;  Zhao, Xia;  Li, He;  Liu, Taoyu;  Fang, Jingyun
Adobe PDF(1626Kb)  |  收藏  |  浏览/下载：78/0  |  提交时间：2022/02/25
Alpine grasslands  Artificial neural network  Climate change  Random forests  Soil organic carbon  Temperate grasslands