HyperSpectral Image Classification Based on Spectral Attention Graph Convolutional Network

KONG Yi; JI Dingzhe; CHENG Yuhu; WANG Xuesong

doi:10.11999/JEIT220204

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1426-1434

KONG Yi, JI Dingzhe, CHENG Yuhu, WANG Xuesong. HyperSpectral Image Classification Based on Spectral Attention Graph Convolutional Network[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1426-1434. doi: 10.11999/JEIT220204

Citation:

KONG Yi, JI Dingzhe, CHENG Yuhu, WANG Xuesong. HyperSpectral Image Classification Based on Spectral Attention Graph Convolutional Network[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1426-1434. doi: 10.11999/JEIT220204

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HyperSpectral Image Classification Based on Spectral Attention Graph Convolutional Network

doi: 10.11999/JEIT220204

School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

Funds: The National Natural Science Foundation of China (62006232, 61976215, 62176259), The Natural Science Foundation of Jiangsu Province (BK20200632)

Received Date: 2022-03-01
Rev Recd Date: 2022-07-31

Available Online: 2022-08-05

Publish Date: 2023-04-10

Abstract

Abstract

In recent years, graph convolutional network has been widely used in hyperspectral image classification because of its feature aggregation mechanism, which can simultaneously represent the features of a single node and neighboring nodes. However, there are many problems in HyperSpectral Images(HSI), such as band redundancy and different spectrum of the same object, which results in the inadequate reliability of the initial graph constructed by directly using the original spectral features, thus leading to the low classification accuracy of hyperspectral images. Therefore, a semi-supervised classification method for hyperspectral images based on Spectral Attention Graph Convolutional Network (SAGCN) is proposed. Firstly, the attention module is used to interact with the local and global information of the spectrum, and realize the adaptive weighting of the spectrum. Then, for the hyperspectral images after spectral weighting, a more accurate nearest neighbor matrix is constructed by using spatial-spectral similarity. Finally, effective feature aggregation of labeled and unlabeled samples is carried out by graph convolution, and the network is trained with the features of labeled samples. Experimental results on three real hyperspectral image datasets including Indian Pines, Kennedy Space Center and Botswana demonstrate the effectiveness of the proposed method.
- HyperSpectral Image(HSI) classification,
- Semi-supervised classification,
- Graph convolution,
- Spectral attention

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References(18)

References

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