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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
力学研究所 [1]
地理科学与资源研究所 [1]
合肥物质科学研究院 [1]
西安光学精密机械研究... [1]
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OAI收割 [4]
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Learning transferable cross-modality representations for few-shot hyperspectral and LiDAR collaborative classification
期刊论文
OAI收割
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2024, 卷号: 126, 页码: 11
作者:
Dai, Mofan
;
Xing, Shuai
;
Xu, Qing
;
Wang, Hanyun
;
Li, Pengcheng
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收藏
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浏览/下载:27/0
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提交时间:2024/02/26
Multimodal remote sensing data
Meta-learning
Few-shot learning
Cross-modality feature learning
A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint
期刊论文
OAI收割
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 卷号: 17, 页码: 10583-10599
作者:
Qiao, Hangfeng
;
Jiang, Huiping
;
Yang, Gang
;
Jing, Faming
;
Sun, Weiwei
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收藏
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浏览/下载:13/0
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提交时间:2024/08/30
Buildings
Remote sensing
Feature extraction
Semantics
Soft sensors
Land surface
Visualization
Deep learning (DL)
multimodal data fusion
remote sensing imagery (RSI)
social sensing data
urban functional zone (UFZ)
Prediction of Vertical Profile of NOx2082; Using Deep Multimodal Fusion Network Based on the Ground-Based 3-D Remote Sensing
期刊论文
OAI收割
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 卷号: 60
作者:
Zhang, Shulin
;
Li, Bo
;
Liu, Lei
;
Hu, Qihou
;
Liu, Haoran
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收藏
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浏览/下载:112/0
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提交时间:2022/01/10
Pollution measurement
Atmospheric modeling
Atmospheric measurements
Predictive models
Solid modeling
Data models
Instruments
3-D prediction of NO2
deep learning neural network
multiaxis differential optical absorption spectroscopy (MAX-DOAS)
multimodal information fusion
remote sensing
Hyperspectral and LiDAR Representation with Spectral-Spatial Graph Network
期刊论文
OAI收割
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 页码: 1-14
作者:
Du, Xingqian
;
Zheng, Xiangtao
;
Lu, Xiaoqiang
;
Wang, Xin
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收藏
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浏览/下载:3/0
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提交时间:2023/11/10
Data fusion
graph neural network
multimodal data
remote sensing classification
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