DPGC: Adversarial Domain Adaptive Network Based on Dynamic Class Prototype and Graph Convolution for Few-Shot Hyperspectral Image Classification

Abstract

Existing cross-domain few-shot hyperspectral image (HSI) classification methods based meta-learning face two main issues: one is that the optimization of class prototypes is insufficient with a small number of samples, resulting in poor balance between inter-class discriminability and intra-class compactness; the other is the lack of dynamic coordination of different fine-grained domain distributions, leading to limited domain adaptability. To address these issues, this paper proposes an adversarial domain adaptive network based on dynamic class prototype and graph convolution (DPGC) with two core strategies to improve the classification accuracy of cross-domain few-shot HSI classification. The first strategy, dynamic class prototype, optimizes the position distribution of class prototypes by adjusting the weights of real and pseudo-labels, thereby widening inter-class distances and compressing intra-class distances. The second strategy, graph-based domain adversarial strategy, extracts local and global domain features and forces the feature extractor to generate domain-independent features through adversarial training, mitigating performance degradation caused by domain distribution differences. Experiments show that DPGC achieves the best classification performance on three hyperspectral datasets and the code is publicly available at https://github.com/tiwchui/DPGC.