Unlabeled Data Guided Partial Label Learning for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

Unlabeled Data Guided Partial Label Learning for Hyperspectral Image Classification

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:

Yang Shujun¹^ORCID,Jia Yuheng²^ORCID,Ding Yao³^ORCID,Wu Xin⁴^ORCID,Hong Danfeng⁵^ORCID

Affiliation:

1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China

2. School of Computer Science and Engineering, and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education, Southeast University, Nanjing, China

3. School of Optical Engineering, Xi’an Research Institute of High Technology, Xi’an, China

4. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China

5. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10439236.pdf?arnumber=10439236

Reference13 articles.

1. Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines

2. Tensor Singular Spectrum Analysis for 3-D Feature Extraction in Hyperspectral Images

3. Tensor-Based Robust Principal Component Analysis With Locality Preserving Graph and Frontal Slice Sparsity for Hyperspectral Image Classification

4. Lateral-Slice Sparse Tensor Robust Principal Component Analysis for Hyperspectral Image Classification

5. A New Spatial–Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation

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