CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification

Recently, the graph convolutional network (GCN) has drawn increasing attention in the hyperspectral image (HSI) classification. Compared with the convolutional neural network (CNN) with fixed square kernels, GCN can explicitly utilize the correlation between adjacent land covers and conduct flexible...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 59; H. 10; S. 8657 - 8671
Hauptverfasser:	Liu, Qichao, Xiao, Liang, Yang, Jingxiang, Wei, Zhihui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Classification Coders Computational modeling Computer applications Convolution Convolutional neural network (CNN) Data Data processing Decoding Deep learning Feature extraction feature fusion graph convolutional network (GCN) graph encoder and decoder hyperspectral image (HSI) classification Hyperspectral imaging Image classification Incompatibility Kernel Learning Machine learning Methods Neural networks Nodes Pixels Regions Training Training data
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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