中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
地理科学与资源研究... [31]
成都山地灾害与环境研... [4]
沈阳自动化研究所 [4]
新疆理化技术研究所 [3]
国家天文台 [3]
植物研究所 [3]
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OAI收割 [61]
内容类型
期刊论文 [59]
会议论文 [2]
发表日期
2019 [61]
学科主题
Forestry [2]
Remote Sen... [2]
Agricultur... [1]
Agronomy [1]
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浏览/检索结果:
共61条,第1-10条
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发表日期:2019
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Unsupervised learning of human action categories in still images with deep representations
期刊论文
OAI收割
ACM Transactions on Multimedia Computing, Communications and Applications, 2019, 卷号: 15, 期号: 4
作者:
Zheng, Yunpeng
;
Li, Xuelong
;
Lu, Xiaoqiang
  |  
收藏
  |  
浏览/下载:38/0
  |  
提交时间:2020/01/17
Action categorization
unsupervised learning
deep representations
A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods
期刊论文
OAI收割
CATENA, 2019, 卷号: 183, 页码: 17
作者:
Zhu, A-Xing
;
Miao, Yamin
;
Liu, Junzhi
;
Bai, Shibiao
;
Zeng, Canying
  |  
收藏
  |  
浏览/下载:26/0
  |  
提交时间:2020/05/19
Landslide absence data
Sampling method
Data-driven methods
Landslide susceptibility mapping, similarity model, data mining and machine learning
A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods
期刊论文
OAI收割
CATENA, 2019, 卷号: 183, 页码: 17
作者:
Zhu, A-Xing
;
Miao, Yamin
;
Liu, Junzhi
;
Bai, Shibiao
;
Zeng, Canying
  |  
收藏
  |  
浏览/下载:10/0
  |  
提交时间:2020/05/19
Landslide absence data
Sampling method
Data-driven methods
Landslide susceptibility mapping, similarity model, data mining and machine learning
Land cover dynamic change in the Napahai Basin using the optimized random forest model
期刊论文
OAI收割
JOURNAL OF APPLIED REMOTE SENSING, 2019, 卷号: 13, 期号: 4, 页码: 18
作者:
Bai, Chunyu
;
He, Hongming
;
Li, Yu
;
He, Wenming
;
Zhao, Hongfei
  |  
收藏
  |  
浏览/下载:30/0
  |  
提交时间:2020/05/19
remote sensing interpretation
optimized random forest model
land cover
wetland landscape
Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data
期刊论文
OAI收割
REMOTE SENSING, 2019, 卷号: 11, 期号: 21, 页码: 20
作者:
Wu, Hua
;
Ying, Wangmin
  |  
收藏
  |  
浏览/下载:18/0
  |  
提交时间:2020/05/19
net surface shortwave radiation
MODIS
FLUXNET
Random Forest
Artificial Neural Network
Support Vector Regression
A Machine Learning Approach to Crater Classification from Topographic Data
期刊论文
OAI收割
REMOTE SENSING, 2019, 卷号: 11, 期号: 21, 页码: 30
作者:
Liu, Qiangyi
;
Cheng, Weiming
;
Yan, Guangjian
;
Zhao, Yunliang
;
Liu, Jianzhong
  |  
收藏
  |  
浏览/下载:27/0
  |  
提交时间:2020/05/19
moon
distinguish primary craters from secondary craters
machine learning
crater characteristics
The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy
期刊论文
OAI收割
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2019, 卷号: 8, 期号: 11, 页码: 17
作者:
Sun, Rui
;
Chen, Shaohui
;
Su, Hongbo
;
Mi, Chunrong
;
Jin, Ning
  |  
收藏
  |  
浏览/下载:36/0
  |  
提交时间:2020/05/19
support vector machine
classification accuracy
remote sensing
random forest
Forest-Type Classification Using Time-Weighted Dynamic Time Warping Analysis in Mountain Areas: A Case Study in Southern China
期刊论文
OAI收割
FORESTS, 2019, 卷号: 10, 期号: 11, 页码: 18
作者:
Cheng, Kai
;
Wang, Juanle
  |  
收藏
  |  
浏览/下载:53/0
  |  
提交时间:2020/05/19
forest type
time-weighted dynamic time warping
random forest
support vector machine
mountain area
southern China
Forest-Type Classification Using Time-Weighted Dynamic Time Warping Analysis in Mountain Areas: A Case Study in Southern China
期刊论文
OAI收割
FORESTS, 2019, 卷号: 10, 期号: 11, 页码: 18
作者:
Cheng, Kai
;
Wang, Juanle
  |  
收藏
  |  
浏览/下载:5/0
  |  
提交时间:2020/05/19
forest type
time-weighted dynamic time warping
random forest
support vector machine
mountain area
southern China
A Machine Learning Approach to Crater Classification from Topographic Data
期刊论文
OAI收割
REMOTE SENSING, 2019, 卷号: 11, 期号: 21, 页码: 30
作者:
Liu, Qiangyi
;
Cheng, Weiming
;
Yan, Guangjian
;
Zhao, Yunliang
;
Liu, Jianzhong
  |  
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2020/05/19
moon
distinguish primary craters from secondary craters
machine learning
crater characteristics