C³DA: A Universal Domain Adaptation Method for Scene Classification From Remote Sensing Imagery-Reference-Cited by-同舟云学术

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C³DA: A Universal Domain Adaptation Method for Scene Classification From Remote Sensing Imagery

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Guo Jiaxu¹^ORCID,Lai Yushan²,Zhang Jinxiao²^ORCID,Zheng Juepeng³^ORCID,Fu Haohuan⁴^ORCID,Gan Lin⁴^ORCID,Hu Liang¹^ORCID,Xu Gaochao¹,Che Xilong¹^ORCID

Affiliation:

1. College of Computer Science and Technology, Jilin University, Changchun, China

2. School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, China

3. Department of Earth System Science, Tsinghua University, Beijing, China

4. National Supercomputing Center in Wuxi, Wuxi, China

Funder

National Natural Science Foundation of China

Jiangsu Innovation Capacity Building Program

National Key Research and Development Plan of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10485440.pdf?arnumber=10485440

Reference26 articles.

1. Universal Domain Adaptation without Source Data for Remote Sensing Image Scene Classification

2. Language-Aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification

3. GEO-WMS: an improved approach to geoscientific workflow management system on HPC

4. Single-Source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification

5. Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PT-ESM: A Parameter-Testing and Integration Framework for Earth System Models Oriented towards High-Performance Computing;Atmosphere;2024-08-05

2. HyUniDA: Breaking Label Set Constraints for Universal Domain Adaptation in Cross-Scene Hyperspectral Image Classification;IEEE Transactions on Geoscience and Remote Sensing;2024

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