The local convolution operation in convolutional neural networks cannot fully learn the global semantic information in hyperspectral images. Given this, this study designed a novel deep network model based on Transformer in order to further improve the classification precision of hyperspectral images. Firstly, this study reduced the dimensionality of hyperspectral images using the principal component analysis method and selected the neighborhood data around pixels as input samples to fully utilize the spatial-spectral information in the images. Secondly, the convolutional layer was used to transform the input samples into sequential characteristic vectors. Finally, image classification was conducted using the designed deep Transformer network. The multi-head attention mechanism in the Transformer model can make full use of the rich discriminative information. Experimental results show that the method proposed in this study can achieve better classification performance than the existing convolutional neural network model.
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