Hyperspectral Image Classification Based on Graph Transformer Network and Graph Attention Mechanism
Author:
Affiliation:
1. Xi’an Research Institute of High Technology, Xi’an, China
2. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Funder
National Natural Science Foundation of China
National Natural Science Foundation of Shaanxi
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology
Link
http://xplorestaging.ieee.org/ielx7/8859/9651998/09793640.pdf?arnumber=9793640
Reference20 articles.
1. Semi-supervised locality preserving dense graph neural network with ARMA filters and context-aware learning for hyperspectral image classification;ding;IEEE Trans Geosci Remote Sens,2021
2. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
3. SLIC super-pixels;achanta,2010
4. Graph transformer networks;yun;Proc Adv Neural Inf Process Syst (NIPS),2019
5. Heterogeneous Graph Attention Network
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