Incremental Multitask Contrastive Learning Network for End-to-End Few-Shot Open-Set Classification of Hyperspectral Images

Hyperspectral image open-set classification has gained increasing attention due to its practical significance. However, existing approaches face two major challenges: (1) poor and unstable classification performance under limited labeled samples, and (2) the lack of end-to-end open-set classification frameworks. To address these issues, we propose an Incremental Multi-Task Contrastive Learning Network (IMTCLN), which integrates four learning tasks to achieve end-to-end open-set classification under few-shot conditions through feature sharing and multi-task collaboration. First, we introduce an expanded class labeling method in the model’s output layer, enabling end-to-end open-set classification. Second, among the four learning tasks, the supervised classification task learns the mapping between known-class samples and their labels using limited labeled data. To enhance classification performance under few-shot conditions, we design a semi-supervised Euclidean contrastive learning task, which improves intra-class compactness and inter-class separability by modeling homogeneous and heterogeneous sample relationships. Additionally, for effective unknown-class recognition, we propose a supervised Mahalanobis contrastive learning task, optimizing the Mahalanobis distance among known classes to identify unknown-class samples. Finally, to further enhance classification stability, we introduce an incremental learning task, which leverages pseudo-labeled unknown-class samples to learn their discriminative features, enabling robust discrimination between known and unknown classes. Extensive experiments on three public datasets demonstrate that IMTCLN significantly outperforms existing methods, particularly under extremely limited labeled samples, showcasing superior open-set classification performance and stability.