Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model

arXiv:2601.22581v2 Announce Type: replace-cross Abstract: Although cross-domain few-shot learning (CDFSL) for hyper-spectral image (HSI) classification has attracted significant research interest, existing works often rely on an unrealistic data augmentation procedure in the form of external noise to enlarge the sample size, thus greatly simplifying the issue of data scarcity. They involve a large number of parameters for model updates, being prone to the overfitting problem. To the best of our