Momentum-Enhanced Dual-Prototype Learning Framework for Robust Few-Shot Hyperspectral Image Classification

Prototypical network-based few-shot learning (FSL) has demonstrated promising performance for hyperspectral image (HSI) classification tasks under scarce sample conditions. However, existing prototype-based FSL methods suffer from data distribution variations among randomly sampled tasks, leading to unstable class prototype representations and weak cross-task generalization with limited samples. To address this issue, we propose a momentum-enhanced dual-prototype learning (MEDPL) framework for robust few-shot HSI classification. Firstly, a momentum-updated prototype mechanism constructs an iteratively optimized prototype memory bank. It obtains accumulated prototypes by exponentially decaying weighted fusion of historical and current prototypes, significantly suppressing noise from randomly sampled data and class center shifts caused by distribution bias. Simultaneously, a class-conditioned perturbation-augmentation strategy is introduced. It generates adaptive noise perturbations for support set features based on learnable covariance matrices to obtain enhanced prototypes, thereby improving the generalization representation capability of class prototypes across tasks. Secondly, a dual-prototype metric learning framework is designed, jointly utilizing accumulated prototypes and enhanced prototypes to synergistically enhance the model’s classification stability and cross-task generalization, thus significantly improving the robustness of few-shot classification. Experimental results demonstrate that MEDPL outperforms other few-shot hyperspectral image classification methods. Our source code is available at https://github.com/hejinrong/MEDPL.