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地理科学与资源研... [317]
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专题:地理科学与资源研究所
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A novel Landsat-based automated mapping of marsh wetland in the headwaters of the Brahmaputra, Ganges and Indus Rivers, southwestern Tibetan Plateau
期刊论文
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 卷号: 103, 页码: 16
作者:
Liu, Qionghuan
;
Zhang, Yili
;
Liu, Linshan
;
Wang, Zhaofeng
;
Nie, Yong
收藏
  |  
浏览/下载:44/0
  |  
提交时间:2021/11/05
Wetland mapping
Random forest
Feature optimization
Google Earth Engine
Tibetan Plateau
Integrating multiple semantics data to assess the dynamic change of urban green space in Beijing, China
期刊论文
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 卷号: 103, 页码: 15
作者:
Yang, Zhiqi
;
Fang, Chuanglin
;
Li, Guangdong
;
Mu, Xufang
收藏
  |  
浏览/下载:69/0
  |  
提交时间:2021/11/05
Urban green space
Temporal segmentation
Random forest
Dynamic change
Social sensing
Beijing
Effectiveness of machine learning methods for water segmentation with ROI as the label: A case study of the Tuul River in Mongolia
期刊论文
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 卷号: 103, 页码: 11
作者:
Li, Kai
;
Wang, Juanle
;
Yao, Jinyi
收藏
  |  
浏览/下载:73/0
  |  
提交时间:2021/11/05
Water Segmentation
Pixel-based CNN
ROI
U-net
Mongolia
A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning
期刊论文
REMOTE SENSING OF ENVIRONMENT, 2021, 卷号: 265, 页码: 19
作者:
Wang, Han
;
Mao, Kebiao
;
Yuan, Zijin
;
Shi, Jiancheng
;
Cao, Mengmeng
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  |  
浏览/下载:57/0
  |  
提交时间:2021/11/05
Land surface temperature (LST)
Model-data-knowledge-driven
Deep learning
Geophysical logical reasoning
Expert knowledge
An integrated index based on climatic constraints and soil quality to simulate vegetation productivity patterns
期刊论文
ECOLOGICAL INDICATORS, 2021, 卷号: 129, 页码: 11
作者:
Geng, Shoubao
;
Li, Wei
;
Kang, Tingting
;
Shi, Peili
;
Zhu, Wanrui
收藏
  |  
浏览/下载:159/0
  |  
提交时间:2021/11/05
Vegetation productivity pattern
Climatic constraint
Soil quality
Water-temperature-soil index
Vegetation type
Spatial classification
the Taihang Mountains
Examining rice distribution and cropping intensity in a mixed single- and double-cropping region in South China using all available Sentinel 1/ 2 images
期刊论文
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 卷号: 101, 页码: 15
作者:
He, Yingli
;
Dong, Jinwei
;
Liao, Xiaoyong
;
Sun, Li
;
Wang, Zhipan
收藏
  |  
浏览/下载:52/0
  |  
提交时间:2021/08/19
Rice mapping
Cropping intensity
Sentinel-1
2
Time series
Google Earth Engine
Semi-Supervised Convolutional Long Short-Term Memory Neural Networks for Time Series Land Cover Classification
期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 17, 页码: 18
作者:
Shen, Jing
;
Tao, Chao
;
Qi, Ji
;
Wang, Hao
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  |  
浏览/下载:55/0
  |  
提交时间:2021/11/05
image classification
Long Short-Term Memory neural network (LSTM)
remote sensing
semi-supervised learning
time series analysis
UAV-based individual shrub aboveground biomass estimation calibrated against terrestrial LiDAR in a shrub-encroached grassland
期刊论文
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 卷号: 101, 页码: 12
作者:
Zhao, Yujin
;
Liu, Xiaoliang
;
Wang, Yang
;
Zheng, Zhaoju
;
Zheng, Shuxia
收藏
  |  
浏览/下载:74/0
  |  
提交时间:2021/08/19
Shrub encroachment
Biomass
Unmanned aerial vehicle (UAV)
Terrestrial laser scanning
Volume
Individual shrub identification
High-accuracy continuous mapping of surface water dynamics using automatic update of training samples and temporal consistency modification based on Google Earth Engine: A case study from Huizhou, China
期刊论文
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2021, 卷号: 179, 页码: 66-80
作者:
Li, Kewei
;
Xu, Erqi
收藏
  |  
浏览/下载:36/0
  |  
提交时间:2021/11/05
Surface water
Time series
Auto-updating of training samples
Temporal consistency detection
Change monitoring
Intra-annual information
Hyperspectral Image Classification Based on Sparse Superpixel Graph
期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 18, 页码: 18
作者:
Zhao, Yifei
;
Yan, Fengqin
收藏
  |  
浏览/下载:58/0
  |  
提交时间:2021/11/05
hyperspectral image
sparse superpixel graph
spectral-spatial classification
discrete potential
big data
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