Multi-branch feature transformation cross-domain few-shot learning for hyperspectral image classification

Abstract

In the field of hyperspectral image (HSI) classification, a source dataset with ample labeled samples is commonly utilized to enhance the classification performance of a target dataset with few labeled samples. Existing few-shot learning (FSL) methods typically assume identical feature distribution in the source and target domains. However, since the classes of samples collected from different regions may vary considerably, it leads to a disparity in the feature distribution. To address the domain distribution shift between the source and target domains, a cross-domain FSL method based on multi-branch feature transformation (MBFT-CFSL) is proposed for HSI classification. First, the spectral–spatial features of the image are extracted by the multi-branch feature fusion module, and the feature diversity is increased using the featurewise transformation layers to boost the generalization performance of the model. Then, the conditional adversarial domain adaptation technique is employed for model training to lessen the impact of domain shift. Finally, the model is optimized by minimizing the maximum mean difference loss function to further diminish the distribution difference between the source and target domains. Experimental results on three distinct hyperspectral datasets validate the effectiveness of MBFT-CFSL, with the overall classification accuracy improved by 1.73%–5.45% compared to the suboptimal method. The source code is available at https://github.com/Ziyin2/MBFT-CFSL.