Hyperspectral Image Classification Using Hierarchical Spatial-Spectral Transformer

In recent years, convolutional neural networks (CNNs) have been successfully applied in hyperspectral image (HSI) classification tasks. However, the spatial-spectral features within an HSI have not been well explored using convolutions in CNNs. In the paper, a novel end-to-end hierarchical spatial-spectral transformer (HSST) is proposed for HSI classification, in which effective spatial-spectral features are emphasized using multi-head self-attention mechanism (MHSA). MHSA module captures better internal correlation of HSI data than the traditional convolution operation and can compute weighting scores for spatial and spectral context of pixels. Furthermore, a hierarchical architecture is designed to reduce a large number of parameters in the original transformer-style networks while still achieving satisfying classification results. Experimental results over two benchmark HSI datasets demonstrated the proposed HSST obviously outperforms several state-of-the-art deep learning-based HSI classification algorithms.