Convolutional Neural Network and Vision Transformer-driven Cross-layer Multi-scale Fusion Network for Hyperspectral Image Classification

ZHAO Feng; GENG Miaomiao; LIU Hanqiang; ZHANG Junjie; YU Jun

doi:10.11999/JEIT231209

Volume 46 Issue 5

May 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(5): 2237-2248

ZHAO Feng, GENG Miaomiao, LIU Hanqiang, ZHANG Junjie, YU Jun. Convolutional Neural Network and Vision Transformer-driven Cross-layer Multi-scale Fusion Network for Hyperspectral Image Classification[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2237-2248. doi: 10.11999/JEIT231209

Citation:

ZHAO Feng, GENG Miaomiao, LIU Hanqiang, ZHANG Junjie, YU Jun. Convolutional Neural Network and Vision Transformer-driven Cross-layer Multi-scale Fusion Network for Hyperspectral Image Classification[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2237-2248. doi: 10.11999/JEIT231209

Citation:

ZHAO Feng, GENG Miaomiao, LIU Hanqiang, ZHANG Junjie, YU Jun. Convolutional Neural Network and Vision Transformer-driven Cross-layer Multi-scale Fusion Network for Hyperspectral Image Classification[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2237-2248. doi: 10.11999/JEIT231209

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Convolutional Neural Network and Vision Transformer-driven Cross-layer Multi-scale Fusion Network for Hyperspectral Image Classification

doi: 10.11999/JEIT231209

1.
School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
3.
University of Science and Technology of China, Hefei 223700, China

Funds: The National Natural Science Foundation of China (62071379, 62071378, 62106196), The Youth Innovation Team of Shaanxi Universities

Received Date: 2023-11-01
Rev Recd Date: 2024-03-31

Available Online: 2024-04-18

Publish Date: 2024-05-30

Abstract

Abstract

HyperSpectral Image (HSI) classification is one of the most prominent research topics in geoscience and remote sensing image processing tasks. In recent years, the combination of Convolutional Neural Network (CNN) and vision transformer has achieved success in HSI classification tasks by comprehensively considering local-global information. Nevertheless, the ground objects of HSIs vary in scale, containing rich texture information and complex structures. The current methods based on the combination of CNN and vision transformer usually have limited capability to extract texture and structural information of multi-scale ground objects. To overcome the above limitations, a CNN and vision transformer-driven cross-layer multi-scale fusion network is proposed for HSI classification. Firstly, from the perspective of combining CNN and visual transformer, a cross-layer multi-scale local-global feature extraction module branch is constructed, which is composed of a convolution embedded vision transformer architecture and a cross-layer feature fusion module. Specifically, to enhance attention to multi-scale ground objects in HSIs, the convolution embedded vision transformer captures multi-scale local-global features effectively by organically combining multi-scale CNN and vision transformer. Furthermore, the cross-layer feature fusion module aggregates hierarchical multi-scale local-global features, thereby combining shallow texture information and deep structural information of ground objects. Secondly, a group multi-scale convolution module branch is designed to explore the potential multi-scale features from abundant spectral bands in HSIs. Finally, to mine local spectral details and global spectral information in HSIs, a residual group convolution module is designed to extract local-global spectral features. Experimental results on Indian Pines, Houston 2013, and Salinas Valley datasets confirm the effectiveness of the proposed method.
- HyperSpectral Image (HSI) classification,
- Convolutional Neural Network (CNN),
- Vision transformer,
- Multi-scale features,
- Fusion network

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References(25)

References

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