Transferable Multiple Subspace Learning for Hyperspectral Image Super-Resolution-Reference-Cited by-同舟云学术

Transferable Multiple Subspace Learning for Hyperspectral Image Super-Resolution

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:

Bu Yuanyang¹^ORCID,Zhao Yongqiang¹^ORCID,Xue Jize²^ORCID,Yao Jiaxin¹^ORCID,Chan Jonathan Cheung-Wai³^ORCID

Affiliation:

1. School of Automation, Northwestern Polytechnical University, Xi’an, China

2. School of Communications and Information Engineering and the School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an, China

3. Department of Electronics and Informatics, Vrije Universiteit Brussel, Brussel, Belgium

Funder

National Natural Science Foundation of China

Key Research and Development Plan of Shaanxi Province

Science, Technology and Innovation Commission of Shenzhen Municipality

Doctor Dissertation of Northwestern Polytechnical University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10342884.pdf?arnumber=10342884

Reference30 articles.

1. Feedback Attention-Based Dense CNN for Hyperspectral Image Classification

2. Morphological Transformation and Spatial-Logical Aggregation for Tree Species Classification Using Hyperspectral Imagery

3. Hyperspectral Image Instance Segmentation Using Spectral–Spatial Feature Pyramid Network

4. NonRegSRNet: a Non-rigid Registration Hyperspectral Super-Resolution Network

5. Enhanced Autoencoders With Attention-Embedded Degradation Learning for Unsupervised Hyperspectral Image Super-Resolution

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