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科研机构
地理科学与资源研... [144]
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期刊论文 [87]
SCI/SSCI论... [39]
EI期刊论文 [11]
会议论文 [7]
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2020 [19]
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专题:地理科学与资源研究所
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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
Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery
期刊论文
ENERGY, 2021, 卷号: 234, 页码: 10
作者:
Wang, Jiaxin
;
Lu, Feng
收藏
  |  
浏览/下载:26/0
  |  
提交时间:2021/11/05
Electricity consumption
Nighttime light
Landscape pattern
Land use
MGWR
Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network
期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 16, 页码: 20
作者:
Ma, Nan
;
Sun, Lin
;
Zhou, Chenghu
;
He, Yawen
收藏
  |  
浏览/下载:24/0
  |  
提交时间:2021/11/05
ASTER spectral library
hyperspectral data
1D convolutional neural network
cloud detection
data simulation
multi-satellite remote sensing images
Mapping quasi-circular vegetation patch dynamics in the Yellow River Delta, China, between 1994 and 2016
期刊论文
ECOLOGICAL INDICATORS, 2021, 卷号: 126, 页码: 9
作者:
Shi, Lei
;
Liu, Qingsheng
;
Huang, Chong
;
Gao, Xing
;
Li, He
收藏
  |  
浏览/下载:76/0
  |  
提交时间:2021/07/09
Quasi-circular vegetation patch
Patch dynamic monitoring
Yellow River Delta
Wetland of international importance
Remote sensing
Spontaneous revegetation
Mapping Panax Notoginseng Plantations by Using an Integrated Pixel- and Object-Based (IPOB) Approach and ZY-3 Imagery
期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 11, 页码: 22
作者:
Yang, Zhiqi
;
Dong, Jinwei
;
Kou, Weili
;
Qin, Yuanwei
;
Xiao, Xiangming
收藏
  |  
浏览/下载:71/0
  |  
提交时间:2021/08/19
Random Forest
integrated pixel- and object-based (IPOB) approach
feature selection
segmentation
Panax notoginseng
Tree counting with high spatial-resolution satellite imagery based on deep neural networks
期刊论文
ECOLOGICAL INDICATORS, 2021, 卷号: 125, 页码: 12
作者:
Yao, Ling
;
Liu, Tang
;
Qin, Jun
;
Lu, Ning
;
Zhou, Chenghu
收藏
  |  
浏览/下载:40/0
  |  
提交时间:2021/06/10
Forestry
Deep learning algorithms
Remote sensing
Density estimation
Tree counting
Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery
期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 10, 页码: 22
作者:
Hu, Minqi
;
Ma, Ronghua
;
Cao, Zhigang
;
Xiong, Junfeng
;
Xue, Kun
收藏
  |  
浏览/下载:22/0
  |  
提交时间:2021/08/19
algal biomass index
inland waters
Landsat-8
trophic state
Mapping Forest Types in China with 10 m Resolution Based on Spectral-Spatial-Temporal Features
期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 5, 页码: 21
作者:
Cheng, Kai
;
Wang, Juanle
;
Yan, Xinrong
收藏
  |  
浏览/下载:45/0
  |  
提交时间:2021/06/10
forest type
spectral–
spatial–
temporal features
image composition
random forest algorithm
China
Mapping China's Electronic Power Consumption Using Points of Interest and Remote Sensing Data
期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 6, 页码: 17
作者:
Jin, Cheng
;
Zhang, Yili
;
Yang, Xuchao
;
Zhao, Naizhuo
;
Ouyang, Zutao
收藏
  |  
浏览/下载:28/0
  |  
提交时间:2021/07/09
electric power consumption
points of interest
nighttime light
random forests
China
Remote sensing inversion of grassland aboveground biomass based on high accuracy surface modeling
期刊论文
ECOLOGICAL INDICATORS, 2021, 卷号: 121, 页码: 10
作者:
Zhou, Wei
;
Li, Haoran
;
Xie, Lijuan
;
Nie, Xuemin
;
Wang, Zong
收藏
  |  
浏览/下载:19/0
  |  
提交时间:2021/03/15
Aboveground biomass
High accuracy surface modelling
Random forest
Driving mechanism
Three-rivers source region
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