Topological Structure and Semantic Information Transfer Network for Cross-Scene Hyperspectral Image Classification

Domain adaptation techniques have been widely applied to the problem of cross-scene hyperspectral image (HSI) classification. Most existing methods use convolutional neural networks (CNNs) to extract statistical features from data and often neglect the potential topological structure information bet...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 34; H. 6; S. 2817 - 2830
Hauptverfasser:	Zhang, Yuxiang, Li, Wei, Zhang, Mengmeng, Qu, Ying, Tao, Ran, Qi, Hairong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.06.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Alignment Artificial neural networks Classification Convolutional neural networks Cross-scene Data mining Data structures distribution alignment domain adaption Domains Feature extraction graph alignment graph convolutional network (GCN) Graph theory hyperspectral image (HSI) classification Hyperspectral imaging Image classification Information transfer Land cover Land use Neural networks Semantics Task analysis Topology Training
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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