Multiscale 3-D–2-D Mixed CNN and Lightweight Attention-Free Transformer for Hyperspectral and LiDAR Classification-Reference-Cited by-同舟云学术

Multiscale 3-D–2-D Mixed CNN and Lightweight Attention-Free Transformer for Hyperspectral and LiDAR Classification

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:

Sun Le¹^ORCID,Wang Xinyu²,Zheng Yuhui²^ORCID,Wu Zebin³^ORCID,Fu Liyong⁴^ORCID

Affiliation:

1. School of Computer Science, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China

2. School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China

3. School of Computer Engineering, Nanjing University of Science and Technology, Nanjing, China

4. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

14th Five-Year Plan Pioneering Project of High Technology Plan of the National Department of Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10440123.pdf?arnumber=10440123

Reference64 articles.

1. Classification via Structure-Preserved Hypergraph Convolution Network for Hyperspectral Image

2. Spectral-Spatial Latent Reconstruction for Open-Set Hyperspectral Image Classification

3. Low Rank Component Induced Spatial-Spectral Kernel Method for Hyperspectral Image Classification

4. Deep Learning for Hyperspectral Image Classification: An Overview

5. A Survey on Hyperspectral Imaging for Mineral Exploration using Machine Learning Algorithms

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