Weighted residual self-attention graph-based transformer for spectral–spatial hyperspectral image classification-Reference-Cited by-同舟云学术

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Weighted residual self-attention graph-based transformer for spectral–spatial hyperspectral image classification

Published:2023-02-01 Issue:3 Volume:44 Page:852-877
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Zu Baokai¹,Wang Hongyuan¹,Li Jianqiang¹,He Ziping²³^ORCID,Li Yafang¹,Yin Zhixian²^ORCID

Affiliation:

1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

2. School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China

3. Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany

Funder

Beijing Natural Science Foundation

Beijing Municipal Science and Technology Project

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2171744

Reference54 articles.

1. Baevski, A., and M. Auli. 2018. “Adaptive Input Representations for Neural Language Modeling.” arXiv preprint arXiv:1809.10853.

2. A new k-nearest neighbor density-based clustering method and its application to hyperspectral images

3. Exploring Hierarchical Convolutional Features for Hyperspectral Image Classification

4. Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

5. Deep Learning-Based Classification of Hyperspectral Data

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DW-D ³ A: dynamic weighted dual-driven domain adaptation for cross-scene hyperspectral image classification;International Journal of Remote Sensing;2024-07

2. Semi-supervised hierarchical Transformer for hyperspectral Image classification;International Journal of Remote Sensing;2023-12-27

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