Fusion of local and important channel information for multi-attention hyperspectral image classification

In recent years, methods based on deep convolutional neural networks (CNNs) have gradually become the focus of research in the field of hyperspectral image (HSI) classification. It is well known that hyperspectral data itself contains spatial and spectral information. While CNN-based methods have advantages in extracting local spatial features, they are not good at handling spectral features and global information. Therefore, this paper proposes a multi-attention network that fuses local and key channel information to complete the task of HSI classification. First, the principal component analysis (PCA) is used to pre-process the HSI data. Second, a feature information fusion module based on the SE module and 2D convolution is constructed to fuse local spatial information and enhanced feature channel information. Third, the global covariance pooling function accelerates the convergence rate of the network. Finally, the fused features are sent to the Vision Transformer (ViT) module for position encoding to capture global sequential information and improve the hyperspectral image classification results. Experiments carried out on several typical three public datasets demonstrate that the proposed network method can provide competitive results compared to the other state-of-the-art HSI networks.