Attention Multihop Graph and Multiscale Convolutional Fusion Network for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

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Attention Multihop Graph and Multiscale Convolutional Fusion Network for Hyperspectral Image Classification

Published:2023 Issue: Volume:61 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zhou Hao¹^ORCID,Luo Fulin²^ORCID,Zhuang Huiping³,Weng Zhenyu¹,Gong Xiuwen⁴^ORCID,Lin Zhiping¹^ORCID

Affiliation:

1. School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore

2. College of Computer Science, Chongqing University, Chongqing, China

3. Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangdong, China

4. Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia

Funder

National Natural Science Foundation of China

Natural Science Foundation of Chongqing

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10098209.pdf?arnumber=10098209

Reference48 articles.

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

2. Multi-feature fusion: Graph neural network and CNN combining for hyperspectral image classification

3. Deep Convolutional Neural Networks for Hyperspectral Image Classification

4. CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification

5. Semantic and spatial‐spectral feature fusion transformer network for the classification of hyperspectral image

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