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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Detalles Bibliográficos

Principais autores:	Qingyan Wang, Lisong Ma, Junping Zhang, Shouqiang Kang, Yujing Wang
Formato:	Artigo
Idioma:	Inglês
Publicado:	IEEE 2024-01-01
Series:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Assuntos:	Feature fusion hyperspectral image (HSI) classification multilevel skip pixels sparse graph attention network (GAT) superpixels
Acceso en liña:	https://ieeexplore.ieee.org/document/10582396/
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