Classification via Structure-Preserved Hypergraph Convolution Network for Hyperspectral Image-Reference-Cited by-同舟云学术

Classification via Structure-Preserved Hypergraph Convolution Network for Hyperspectral Image

Published:2023 Issue: Volume:61 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Duan Yule¹,Luo Fulin²^ORCID,Fu Maixia¹^ORCID,Niu Yingying¹,Gong Xiuwen³^ORCID

Affiliation:

1. Key Laboratory of Grain Information Processing and Control, Education Ministry of China, and the College of Information Science and Engineering, Henan University of Technology, Zhengzhou, China

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

3. Faculty of Engineering, The University of Sydney, Camperdown, NSW, Australia

Funder

National Natural Science Foundation of China

Key Scientific Research Projects of Colleges and Universities in Henan Province

Research Foundation for Advanced Talents of Henan University of Technology

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/ielx7/36/10006360/10076482.pdf?arnumber=10076482

Reference42 articles.

1. Graph Convolutional Networks for Hyperspectral Image Classification

2. Sparse-Adaptive Hypergraph Discriminant Analysis for Hyperspectral Image Classification

3. Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification

4. Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities

5. Graph Convolutional Network Based on Manifold Similarity Learning

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