Different from the existing CNN-based models, a novel method based on the transformer model is proposed in this paper, to further improve the classification accuracy of hyperspectral image (HSI). Specifically, a deep network model is constructed with the Transformer-iN-Transformer (TNT) modules, to carry out end-to-end classification. The outer and inner transformer models in the TNT module can extract the patch-level and pixel-level features respectively, to make full use of the global and local information in the input cubes. Experimental results show that the proposed method can achieve better classification performance than the existing CNN-based models. In addition, using the transformer-baesd deep model without convolution to classify HSI provides a new idea for the related researches.