Spatial–Spectral 1DSwin Transformer With Groupwise Feature Tokenization for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

Spatial–Spectral 1DSwin Transformer With Groupwise Feature Tokenization for Hyperspectral Image Classification

Published:2023 Issue: Volume:61 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:

Xu Yifei¹^ORCID,Xie Yixuan¹,Li Bicheng¹,Xie Chuanqi²,Zhang Yongchuan³,Wang Aichen⁴^ORCID,Zhu Li¹^ORCID

Affiliation:

1. School of Software, Xi’an Jiaotong University, Xi’an, China

2. State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China

3. Chongqing Smart City Institute, Chongqing Jiaotong University, Chongqing, China

4. Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang, China

Funder

National Key Research and Development Project

Young Scientists Fund of the National Natural Science Foundation of China

China Postdoctoral Science Foundation Funded Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10178075.pdf?arnumber=10178075

Reference57 articles.

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2. Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox

3. CuDNN: Efficient primitives for deep learning;chetlur;Arxiv 1410 0759,2014

4. Deep Ensemble CNN Method Based on Sample Expansion for Hyperspectral Image Classification

5. Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks

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