Few-Shot Learning With Prototype Rectification for Cross-Domain Hyperspectral Image Classification

Deep learning has been extensively applied to hyperspectral image (HSI) classification and has achieved significant success. However, the number of labeled samples available for HSI classification tasks is typically limited in practical applications, which makes the high-accuracy of HSI small-sample classification still a challenging research task. Therefore, metric-based prototypical networks for few-shot learning (FSL) have become increasingly popular. However, the majority of existing FSL methods typically have problems with biased prototypes and domain shifts. To address these issues, this article proposed a prototype rectification network framework for cross-domain few-shot HSI classification. Specifically, to obtain more representative prototypes, we designed a query-guided prototype rectification module, which can rectify the feature distribution of the support set prototype and obtain a more representative prototype for subsequent training tasks. Then, we introduced a prototype-based interclass loss function to alleviate the interclass confusion that may result from prototype rectification. Furthermore, we construct an intermediate domain between the source domain and the target domain to alleviate domain shift, which helps mitigate the difficulties of domain transfer and achieve a more comprehensive domain alignment. The experimental results on four publicly available HSI datasets demonstrate that our proposed method outperforms the existing FSL methods.