1. SATNet: A Spatial Attention Based Network for Hyperspectral Image Classification.
- Author
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Hong, Qingqing, Zhong, Xinyi, Chen, Weitong, Zhang, Zhenghua, Li, Bin, Sun, Hao, Yang, Tianbao, and Tan, Changwei
- Subjects
FEATURE extraction ,CLASSIFICATION ,RETINAL blood vessels ,MACHINE learning ,PROBLEM solving - Abstract
In order to categorize feature classes by capturing subtle differences, hyperspectral images (HSIs) have been extensively used due to the rich spectral-spatial information. The 3D convolution-based neural networks (3DCNNs) have been widely used in HSI classification because of their powerful feature extraction capability. However, the 3DCNN-based HSI classification approach could only extract local features, and the feature maps it produces include a lot of spatial information redundancy, which lowers the classification accuracy. To solve the above problems, we proposed a spatial attention network (SATNet) by combining 3D OctConv and ViT. Firstly, 3D OctConv divided the feature maps into high-frequency maps and low-frequency maps to reduce spatial information redundancy. Secondly, the ViT model was used to obtain global features and effectively combine local-global features for classification. To verify the effectiveness of the method in the paper, a comparison with various mainstream methods on three publicly available datasets was performed, and the results showed the superiority of the proposed method in terms of classification evaluation performance. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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