Dynamic Spectral Guided Spatial Sparse Transformer for Hyperspectral Image Reconstruction-Reference-Cited by-同舟云学术

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Dynamic Spectral Guided Spatial Sparse Transformer for Hyperspectral Image Reconstruction

Published:2024 Issue: Volume:17 Page:15494-15511
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:

Wang Junyang¹,Yan Xiang¹^ORCID,Qin Hanlin¹^ORCID,Akhtar Naveed²^ORCID,Yang Shuowen¹^ORCID,Mian Ajmal³^ORCID

Affiliation:

1. School of Optoelectronic Engineering, Xidian University, Xi'an, China

2. School of Computing and Information Systems, Faculty of Engineering and IT, The University of Melbourne, Parkville, VIC, Australia

3. Department of Computer Science and Software Engineering, The University of Western Australia, Crawley, WA, Australia

Funder

Higher Education Discipline Innovation Project

Shaanxi Province Key Research and Development Plan Project

Shaanxi Province Science and Technology Plan Project

Xian City Science and Technology Plan Project

Aeronautical Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/4609443/10330207/10659360.pdf?arnumber=10659360

Reference91 articles.

1. Deep Learning for Hyperspectral Image Classification: An Overview

2. Large Kernel Spectral and Spatial Attention Networks for Hyperspectral Image Classification

3. Hyperspectral Image Classification Method Based on CNN Architecture Embedding With Hashing Semantic Feature

4. LSSMA: Lightweight Spectral–Spatial Neural Architecture With Multiattention Feature Extraction for Hyperspectral Image Classification

5. DBCTNet: Double Branch Convolution-Transformer Network for Hyperspectral Image Classification

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