Cross-domain hyperspectral image classification based on transformer

Small-sample-size problem is a big challenge in hyperspectral image (HSI) classification. Deep learning-based methods, especially Transformer, may need more training samples to train a satisfactory model. Cross-domain classification has been proven to be effective in handling the small-sample-size problem. In two HSI scenes sharing the same land-cover classes, one with sufficient labeled samples is called the source domain, while the other with limited labeled samples is called the target domain. Thus, the information on the source domain could help the target domain improve classification performance. This paper proposes a cross-domain Vision Transformer (CD-ViT) method for heterogeneous HSI classification. CD-ViT maps the source samples to the target domain for supplementing training samples. In addition, cross-attention is used to align the source and target features. Moreover, knowledge distillation is employed to learn more transferable information. Experiments on three different cross-domain HSI datasets demonstrate the effectiveness of the proposed approach.