Spectral–Spatial Feature Tokenization Transformer for Hyperspectral Image Classification

In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover category. In the recent past, convolutional neural network (CNN)-based HSI classification methods have greatly improved performance due to their superior ability to represent features. However, these methods have limited ability to obtain deep semantic features, and as the layer’s number increases, computational costs rise significantly. The transformer framework can represent high-level semantic features well. In this article, a spectral–spatial feature tokenization transformer (SSFTT) method is proposed to capture spectral–spatial features and high-level semantic features. First, a spectral–spatial feature extraction…

• NN
  National Natural Science Foundation of China

  Awards: U20B2065, U20B2061, 61971233, 62076137
• NS
  Natural Science Foundation of Jiangsu Province

  Award: BK 20211539