Multilevel Prototype Alignment for Cross-Domain Few-Shot Hyperspectral Image Classification

Hyperspectral image (HSI) classification holds significant application value in precision agriculture, environmental monitoring, and other fields. However, the high cost of large-scale labeling limits the widespread application of deep learning methods. Utilizing a large amount of labeled HSI data for auxiliary training of cross-domain few-shot learning (FSL) methods is an effective means to address this issue. Yet, in practical applications, spectral and spatial features vary across different scenarios, posing significant challenges to the generalizability and accuracy of cross-domain learning models. To tackle this problem, this article proposes a multilevel prototype alignment (MLPA) method for cross-domain few-shot classification, which adjusts feature representations by implementing multilevel feature alignment strategies at various hierarchical levels of the feature extraction network. This approach achieves fine-grained alignment of the source and target domain feature distributions, effectively reducing domain shift and enhancing the model's generalization capability on target domain data. Furthermore, by employing class prototype-based domain adversarial training, the method aligns the prototypes of the source and target domains without simply aligning the entire feature space, thus avoiding overlap in the feature distribution of different classes within the domain and mitigating negative transfer. The MLPA method effectively enhances the generalizability and discriminative power of features in the target domain, thereby improving the performance of cross-domain HSI classification. Experimental results demonstrate that MLPA outperforms other cross-domain few-shot HSI classification methods. Our source code is available at https://github.com/hejinrong/MLPA.