Double-Branch Multilevel Skip Sparse Graph Attention Network for Hyperspectral Image Classification

Graph neural networks (GNNs) offer a potent framework for learning and representing data with graph structures. In the context of hyperspectral images (HSIs) classification tasks, GNNs have been successfully employed. Among the diverse variations of GNNs, graph attention networks (GATs) stand out, d...

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Bibliographic Details

Published in:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Main Authors:	Qingyan Wang, Lisong Ma, Junping Zhang, Shouqiang Kang, Yujing Wang
Format:	Article
Language:	English
Published:	IEEE 2024-01-01
Subjects:	Feature fusion hyperspectral image (HSI) classification multilevel skip pixels sparse graph attention network (GAT) superpixels
Online Access:	https://ieeexplore.ieee.org/document/10582396/