Open-Set Hyperspectral Image Classification via Meta-Learning and Prototype-Guided Decoding

Hyperspectral image classification in open environments remains challenging because models must accurately recognize known categories while reliably identifying samples from previously unseen classes. To address this problem, this paper proposes a new learning framework named Meta Guided Intrinsic Prototype Network (MGIPNet) that improves robustness and adaptability in open-set hyperspectral classification. The proposed method introduces a meta-guided network that learns to adjust its feature extraction process across tasks, enabling better generalization under varying spectral conditions. To strengthen representation stability, intrinsic reference patterns are learned from known data and used to guide feature reconstruction, encouraging compact and consistent representations for known classes while amplifying deviations caused by unknown samples. In addition, a token based aggregation strategy is employed to compress high dimensional spatial information into a compact and informative representation, reducing redundancy while preserving essential spectral-spatial characteristics. These components are jointly optimized through complementary objectives to balance accurate classification and reliable unknown detection. Experiments conducted on multiple benchmark hyperspectral datasets demonstrate that the proposed approach consistently outperforms existing methods in terms of classification accuracy, stability across classes, and detection of unknown samples. Experimental results on the Pavia University dataset show that the proposed framework reaches an overall accuracy of 97.17% and a novel-class detection accuracy of 85.04%, confirming that the joint use of adaptive optimization, prototype-guided representation learning, and compact feature abstraction is effective for open-set hyperspectral image classification.