Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network

doi:10.3390/app13127143

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	Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network
	L. Y. Gao; S. L. Xiao; C. H. Hu and Y. Yan
	2023
发表期刊	Applied Sciences-Basel
卷号	13 期号:12 页码:17
摘要	Convolutional neural networks (CNNs) have attracted significant attention as a commonly used method for hyperspectral image (HSI) classification in recent years; however, CNNs can only be applied to Euclidean data and have difficulties in dealing with relationships due to their limitations of local feature extraction. Each pixel of a hyperspectral image contains a set of spectral bands that are correlated and interact with each other, and the methods used to process Euclidean data cannot effectively obtain these correlations. In contrast, the graph convolutional network (GCN) can be used in non-Euclidean data but usually leads to over-smoothing and ignores local detail features due to the need for superpixel segmentation processing to reduce computational effort. To overcome the above problems, we constructed a fusion network based on the GCN and CNN which contains two branches: a graph convolutional network based on superpixel segmentation and a convolutional network with an added attention mechanism. The graph convolutional branch can extract the structural features and capture the relationships between the nodes, and the convolutional branch can extract detailed features in the local fine region. Owing to the fact that the features extracted from the two branches are different, the classification performance can be improved by fusing the complementary features extracted from the two branches. To validate the proposed algorithm, experiments were conducted on three widely used datasets, namely Indian Pines, Pavia University, and Salinas. An overall accuracy of 98.78% was obtained in the Indian Pines dataset, and overall accuracies of 98.99% and 98.69% were obtained in the other two datasets. The results show that the proposed fusion network can obtain richer features and achieve a high classification accuracy.
DOI	10.3390/app13127143
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收录类别	sci
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/67472
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	L. Y. Gao,S. L. Xiao,C. H. Hu and Y. Yan. Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network[J]. Applied Sciences-Basel,2023,13(12):17.
APA	L. Y. Gao,S. L. Xiao,&C. H. Hu and Y. Yan.(2023).Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network.Applied Sciences-Basel,13(12),17.
MLA	L. Y. Gao,et al."Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network".Applied Sciences-Basel 13.12(2023):17.

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