[2601.22581] Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model

Computer Science > Computer Vision and Pattern Recognition arXiv:2601.22581 (cs) [Submitted on 30 Jan 2026 (v1), last revised 7 Apr 2026 (this version, v2)] Title:Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model Authors:Naeem Paeedeh, Mahardhika Pratama, Ary Shiddiqi, Zehong Cao, Mukesh Prasad, Wisnu Jatmiko View a PDF of the paper titled Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model, by Naeem Paeedeh and 5 other authors View PDF HTML (experimental) 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 knowledge, none has explored the strength of the foundation model, having strong generalization power to be quickly adapted to downstream tasks. This paper proposes the MIxup FOundation MOdel (MIFOMO) for CDFSL of HSI classifications. MIFOMO is built upon the concept of a remote sensing (RS) foundation model, pre-trained across a large scale of RS problems, thus featuring generalizable features. The notion of coalescent projection (CP) is introduced to quickly adapt the foundati...