Hyperspectral Image Super-Resolution via Sparsity Regularization-Based Spatial-Spectral Tensor Subspace Representation-Reference-Cited by-同舟云学术

Hyperspectral Image Super-Resolution via Sparsity Regularization-Based Spatial-Spectral Tensor Subspace Representation

Published:2024 Issue: Volume:17 Page:11707-11722
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Peng Yidong¹^ORCID,Li Weisheng¹^ORCID,Luo Xiaobo²^ORCID,Du Jiao³^ORCID

Affiliation:

1. Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China

2. Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

3. School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China

Funder

Special Funding for Postdoctoral Research Projects of Chongqing

National Natural Science Foundation of China

Natural Science Foundation of Chongqing

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/4609443/10330207/10557732.pdf?arnumber=10557732

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2. A review on the combination of deep learning techniques with proximal hyperspectral images in agriculture

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4. Symmetrical Feature Propagation Network for Hyperspectral Image Super-Resolution

5. Multiscale Factor Joint Learning for Hyperspectral Image Super-Resolution