Causal Meta-Transfer Learning for Cross-Domain Few-Shot Hyperspectral Image Classification

Few-shot hyperspectral image (HSI) classification poses challenges due to sample selection bias in few-shot scenarios, potentially leading to incorrect statistical associations between noncausal factors and category semantics. To address these challenges, an original HSI is treated as a mixture comprising causal and noncausal factors. By integrating the causal learning, meta-learning, and transfer learning, a cross domain few-shot HSI classification method based on causal meta-transfer learning (CMTL) is developed. First, a Mask Transformer is implemented to identify noncausal factors unrelated to categories. Second, an independent causal constraint is applied to separate the causal and noncausal factors, and enhancing the inclusion of pure and independent causal factors in the features. Finally, the meta-transfer learning is leveraged to enable the classification model to extract causal factors highly correlated with category semantics from data, facilitating the cross-domain knowledge transfer. Meanwhile, a causal association module is employed to maximize the mutual information between causal factors and category predictions, thereby ensuring a strong causal association between causal factors and classification tasks. Experimental results show that CMTL achieves competitive performance in cross-domain few-shot HSI classification tasks.