Pseudolabel-Based Unreliable Sample Learning for Semi-Supervised Hyperspectral Image Classification-Reference-Cited by-同舟云学术

Pseudolabel-Based Unreliable Sample Learning for Semi-Supervised Hyperspectral Image Classification

Published:2023 Issue: Volume:61 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Yao Huaxiong¹,Chen Renyi¹^ORCID,Chen Wenjing²^ORCID,Sun Hao¹^ORCID,Xie Wei¹^ORCID,Lu Xiaoqiang³^ORCID

Affiliation:

1. Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, School of Computer Science, National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, China

2. School of Computer Science, Hubei University of Technology, Wuhan, China

3. College of Physics and Information Engineering, Fuzhou University, Fuzhou, China

Funder

National Natural Science Foundation of China

Hubei Provincial Natural Science Foundation of China

Knowledge Innovation Program of Wuhan-Shuguang

Fundamental Research Funds for the Central Universities

National Key Research and Development Program of China

National Science Fund for Distinguished Young Scholars

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10274689.pdf?arnumber=10274689

Reference73 articles.

1. A Supervised Segmentation Network for Hyperspectral Image Classification

2. A simple framework for contrastive learning of visual representations;chen;Proc Int Conf Mach Learn,2020

3. A Two-Stage Convolutional Sparse Coding Network for Hyperspectral Image Classification

4. A 3-D-Swin Transformer-Based Hierarchical Contrastive Learning Method for Hyperspectral Image Classification

5. Physically Constrained Transfer Learning Through Shared Abundance Space for Hyperspectral Image Classification

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