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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Autori principali:	Qingyan Wang, Lisong Ma, Junping Zhang, Shouqiang Kang, Yujing Wang
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IEEE 2024-01-01
Serie:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Soggetti:	Feature fusion hyperspectral image (HSI) classification multilevel skip pixels sparse graph attention network (GAT) superpixels
Accesso online:	https://ieeexplore.ieee.org/document/10582396/
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