Learnable Representative Coefficient Image Denoiser for Hyperspectral Image-Reference-Cited by-同舟云学术

Learnable Representative Coefficient Image Denoiser for Hyperspectral Image

Published:2024 Issue: Volume:62 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:

Peng Jiangjun¹^ORCID,Wang Hailin²^ORCID,Cao Xiangyong³^ORCID,Zhao Qian²^ORCID,Yao Jing⁴^ORCID,Zhang Hongying²^ORCID,Meng Deyu²^ORCID

Affiliation:

1. School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, China

2. School of Mathematics and Statistics and the Key Laboratory of Intelligent Networks and Network Security, Ministry of Education, Xi’an Jiaotong University, Xi’an, Shaanxi, China

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

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

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

“Belt and Road” Innovative Talent Exchange Project for Foreign Experts

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10413643.pdf?arnumber=10413643

Reference61 articles.

1. Hyperspectral Image Classification With Convolutional Neural Network and Active Learning

2. Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation

3. Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior

4. Nonconvex-Sparsity and Nonlocal-Smoothness-Based Blind Hyperspectral Unmixing

5. Three decades of hyperspectral remote sensing of the Earth: A personal view

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