Hyperspectral Image Classification Using Groupwise Separable Convolutional Vision Transformer Network

Recently, Vision Transformer (ViT)-based deep learning models have achieved remarkable performance gains in hyperspectral image classification (HSIC) due to their abilities to model long-range dependencies and extract global spatial features. However, ViT is built with a stack of Transformer blocks and faces the challenge of learning a large number of parameters when processing hyperspectral data. Besides, the inherent modeling of global correlation in Transformer ignores the effective representation of local spatial and spectral features. To address these issues, we propose a lightweight ViT network known as Groupwise Separable Convolutional Vision Transformer (GSC-ViT). Firstly, a Groupwise Separable…

• NN
  National Natural Science Foundation of China

  Awards: 62271130, T2225019
• MS
  Medical Science and Technology Foundation of Guangdong Province

  Award: 2021A0101180005
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2023A1515010066