Low-Rank Prompt-Guided Transformer for Hyperspectral Image Denoising-Reference-Cited by-同舟云学术

Low-Rank Prompt-Guided Transformer for Hyperspectral Image Denoising

Published:2024 Issue: Volume:62 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Tan Xiaodong¹^ORCID,Shao Mingwen¹^ORCID,Qiao Yuanjian¹^ORCID,Liu Tiyao¹^ORCID,Cao Xiangyong²^ORCID

Affiliation:

1. Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong, China

2. School of Computer Science and Technology and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10557690.pdf?arnumber=10557690

Reference68 articles.

1. Low-Rank and Sparse Representation for Hyperspectral Image Processing: A Review

2. Vision Transformer With Contrastive Learning for Hyperspectral Image Classification

3. Dual-stream GNN fusion network for hyperspectral classification

4. Joint spectral-spatial hyperspectral image classification based on hierarchical subspace switch ensemble learning algorithm

5. Dictionary cache transformer for hyperspectral image classification

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