Fractional-Domain Information-Enhanced Hyperspherical Prototype Learning Method for Hyperspectral Image Open-Set Classification

In recent years, research in the field of hyperspectral image classification (HSIC) has increasingly focused on the open-set problem. Open-set classification (OSC) demands not only accurately classifying the known categories but also identifying the unknown samples that are not labeled or included within the training data during the testing stage. Existing open-set methods often suffer from misclassification between the known and unknown categories due to their inadequate utilization of metric space. Moreover, relying on a single-threshold strategy performs poorly in identifying unknown categories in complex open environments. In this article, a fractional-domain information-enhanced hyperspherical prototype learning (FrHSPL) method is proposed for hyperspectral image (HSI) OSC. FrHSPL develops a hyperspherical prototype learning (HSPL) strategy that ensures the features of known categories are uniformly distributed on the hypersphere. Therefore, HSPL can effectively enhance the interclass separability and optimize the exploitation of metric space. Subsequently, to enhance the discrimination capability of spectral features, a frequency-spatial-spectral information aggregation (FSSIA) module is devised to deeply integrate fractional domain information with spatial and spectral information. Finally, an open-set recognition (OSR) module is designed to identify unknown categories by using the prototypes of each known category along with the corresponding prototype radii. Extensive experiments on four common HSI datasets indicate that the proposed FrHSPL exhibits superior performance in comparison with both closed-set and open-set methods.