Beyond Spectral Shift Mitigation: Knowledge Swap Net for Cross-Domain Few-Shot Hyperspectral Image Classification

Spectral shifts between source and target domains (TDs) pose significant challenges in cross-domain hyperspectral image classification (HSIC). Current methods often struggle to balance mitigating these shifts while preserving crucial TD information, which limits their ability to leverage spectral priors and domain-specific characteristics for accurate classification. Our work proposes a novel knowledge swap net (KSN) for few-shot cross-domain HSIC. KSN tackles the challenge by enabling effective knowledge transfer between homogeneous (spectral) and heterogeneous (domain-specific) feature spaces through a two-step knowledge swap strategy: leveraging homogeneous knowledge distillation (Homo-KD) for transferring spectral knowledge and heterogeneous meta-learning (Hetero-ML) for model refinement with TD feedback. In addition, we develop a relative distance difference (RDD) loss function to improve feature discriminability under few-shot conditions. Experiments conducted on four target datasets demonstrate the superiority of KSN. Notably, KSN achieves a remarkable overall accuracy (OA) of 82.56% on the Houston University (HU) 2013 dataset, surpassing other leading methods by 3.83%–8.98%. The source code will be available online: https://github.com/ZhaohuiXue/KSN.