Multisource Domain Generalization Two-Branch Network for Hyperspectral Image Cross-Domain Classification

In practical applications, due to the high cost and difficulty of hyperspectral image (HSI) annotation, labels for the target domain (TD) may be either unavailable or insufficient in quantity. To address this issue, we propose a multi-source domain generalization two-branch network (MDGTnet) and train the model only using source domain (SD) HSIs with contrastive learning to classify an unknown TD image. MDGTnet consists of a classifier and two branches, which are intra-domain uniqueness extraction branch (intra-DUEB) and inter-domain commonality extraction branch (inter-DCEB). The intra-DUEB is responsible for mining internal attributes of each SD, which can be seen as imaging environmental characteristics. And the inter-DCEB is applied to extract generic features among different SDs. The features extracted by two branches are fused at different levels respectively to remove the influence of different imaging environments for discriminative class features. We have conducted extensive experiments on four public HSI datasets. The results show that the proposed method outperforms state-of-the-art methods. It can learn robust models and extract highly discriminative features, leading to excellent performance in HSI cross-domain classification. Especially on the Pavia Center dataset, the overall accuracy (OA) is 2.47% higher and kappa coefficient is 2.92% higher than the best results of the other methods. The code will be released soon on the site of https://github.com/Cherrieqi/MDGTnet.