DRLO: Deep Representation Learning for Large Scale Off-track Satellite Remote Sensing Data-Reference-Cited by-同舟云学术

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DRLO: Deep Representation Learning for Large Scale Off-track Satellite Remote Sensing Data

Published:2023-12-15 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Big Data (BigData)
language:
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Author:

Huang Xin¹,Wang Chenxi²,Zhang Wenbin³,Purushotham Sanjay⁴,Wang Jianwu⁴

Affiliation:

1. Towson University,Department of Computer and Information Sciences,Baltimore,MD,USA

2. University of Maryland, Baltimore County,Goddard Earth Sciences Technology and Research (GESTAR) II,Baltimore,MD,USA

3. Michigan Technology University,Department of Computer Science,Houghton,MI,USA

4. University of Maryland, Baltimore County,Department of Information Systems,Baltimore,MD,USA

Funder

National Science Foundation

National Aeronautics and Space Administration

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385234/10386078/10386306.pdf?arnumber=10386306

Reference31 articles.

1. The CloudSat Mission and the A-Train: A Revolutionary Approach to Observing Earth's Atmosphere

2. Geodesic flow kernel for unsupervised domain adaptation

3. Unsupervised Visual Domain Adaptation Using Subspace Alignment

4. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification;Blitzer

5. Discriminative instance weighting for domain adaptation in statistical machine translation;Foster

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