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Open AccessArticle
A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification
by
Pan Yang
Pan Yang 1,2 and
Xinxin Zhang
Xinxin Zhang 1,2,*
1
College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
2
Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen University of Technology, Xiamen 361024, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(14), 4760; https://doi.org/10.3390/s24144760 (registering DOI)
Submission received: 4 June 2024
/
Revised: 12 July 2024
/
Accepted: 19 July 2024
/
Published: 22 July 2024
Abstract
Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC). However, limited training data and spectral uncertainty restrict the classification performance, and the computational demands of a graph convolution network (GCN) present challenges for real-time applications. To overcome these issues, a dual-branch fusion of a GCN and convolutional neural network (DFGCN) is proposed for HSIC tasks. The GCN branch uses an adaptive multi-scale superpixel segmentation method to build fusion adjacency matrices at various scales, which improves the graph convolution efficiency and node representations. Additionally, a spectral feature enhancement module (SFEM) enhances the transmission of crucial channel information between the two graph convolutions. Meanwhile, the CNN branch uses a convolutional network with an attention mechanism to focus on detailed features of local areas. By combining the multi-scale superpixel features from the GCN branch and the local pixel features from the CNN branch, this method leverages complementary features to fully learn rich spatial–spectral information. Our experimental results demonstrate that the proposed method outperforms existing advanced approaches in terms of classification efficiency and accuracy across three benchmark data sets.
Share and Cite
MDPI and ACS Style
Yang, P.; Zhang, X.
A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification. Sensors 2024, 24, 4760.
https://doi.org/10.3390/s24144760
AMA Style
Yang P, Zhang X.
A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification. Sensors. 2024; 24(14):4760.
https://doi.org/10.3390/s24144760
Chicago/Turabian Style
Yang, Pan, and Xinxin Zhang.
2024. "A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification" Sensors 24, no. 14: 4760.
https://doi.org/10.3390/s24144760
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