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.

Apple core browning not only affects the nutritional quality of apples, but also poses a health risk to consumers. Therefore, there is an urgent need to develop a fast and reliable non-destructive detection method for apple core browning. To deal with the challenges of the long incubation period, strong infectivity, and difficulty in the prevention and control of apple core browning, a novel non-destructive detection method for apple core browning has been developed through combining hyperspectral imaging and dielectric techniques. To reduce the computational complexity of high-dimensional multi-view data, canonical correlation analysis is employed for feature dimensionality reduction. Then, the two low-dimensional vectors extracted from two different sensors are concatenated into one united feature vector; therefore, the information contained in the hyperspectral and dielectric data is fused to improve the detection accuracy of the non-destructive method. At last, five traditional classifiers, such as k-Nearest Neighbors, a support vector machine with radial basis function kernel and polynomial kernel, Decision Tree, and neural network, are trained on the fused feature vectors to discriminate apple core browning. The experimental results on our own constructed dataset have shown that the sensitivity, specificity, and precision of SVM with RBF kernel based on concatenated 70-dimensional feature vectors extracted via canonical correlation analysis reached 99.98%, 99.70%, and 99.70%, respectively, which achieved better results than other models. This study can provide theoretical assurance and technical support for further development of higher accuracy and lower-cost non-destructive detection devices for apple core browning.