Semi-supervised hierarchical Transformer for hyperspectral Image classification-Reference-Cited by-同舟云学术

Semi-supervised hierarchical Transformer for hyperspectral Image classification

Published:2023-12-27 Issue:1 Volume:45 Page:21-50
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

He Ziping¹^ORCID,Zhu Qianglin¹,Xia Kewen²^ORCID,Ghamisi Pedram³⁴^ORCID,Zu Baokai⁵

Affiliation:

1. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China

2. School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China

3. Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany

4. AI4RS (artificial intelligence for remote sensing), Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria

5. Faculty of Information Technology, Beijing University of Technology, Beijing, China

Funder

Hebei Province Natural Science Foundation

Key Research and Development Project from Hebei Province

the General Project of Science and Technology of Beijing Municipal Education Commission

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2290996

Reference72 articles.

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4. Berthelot, D., N. Carlini, I. Goodfellow, A. Oliver, N. Papernot, and C. Raffel. 2019. “MixMatch: A Holistic Approach to Semi-Supervised Learning”. In 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019, vol. 32, Vancouver, BC, Canada. December 8, 2019–December 14, 2019.

5. Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning

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