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- W4313133079 abstract "Deep learning methods have been widely used in hyperspectral image classification (HSIC). In recent years, Convolutional Neural Network (CNN) has become a mainstream model of deep learning for HSIC. Although the CNN-based method has made great progress, it still faces a series of challenges such as insufficient use of long-distance information, limited receiving domain, and high computational overhead. In order to overcome these issues, this paper proposes a multi-level spectral-spatial transformer network (MSTNet) for HSIC through the image-based classification framework. The proposed network learns feature representation through a transformer encoder, and integrates multi-level features through a decoder to generate classification results. Finally, the experimental results on two real hyperspectral data sets verified the superiority of the method." @default.
- W4313133079 created "2023-01-06" @default.
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- W4313133079 date "2022-09-13" @default.
- W4313133079 modified "2023-10-06" @default.
- W4313133079 title "Hyperspectral Image Classification Based on Multi-Level Spectral-Spatial Transformer Network" @default.
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- W4313133079 doi "https://doi.org/10.1109/whispers56178.2022.9955116" @default.
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