HyperEDL: Spectral-Spatial Evidence Deep Learning for Cross-Scene Hyperspectral Image Classification

Cross-scene hyperspectral image (HSI) classification presents significant challenges due to domain shifts, which amplify epistemic uncertainty and lead to substantial performance drops in unseen scenes. While evidence deep learning (EDL) has shown promise in modeling uncertainty, existing methods fall short, as they do not explicitly account for the epistemic uncertainty arising from spatial-spectral feature interactions. To address these challenges, we propose the spectral-spatial evidence deep learning for cross-scene hyperspectral image classification (HyperEDL) framework, which introduces the spatial-spectral multiorder aggregation module (SS-Moga). This module effectively captures and adaptively encodes multiorder contextual interactions from both spatial and spectral perspectives. By combining multiorder contextual encoding with spatial-spectral confidence, our approach fully aggregates multiorder evidence to mitigate epistemic uncertainty arising from knowledge gaps between seen and unseen scenes. Specifically, it uses Dirichlet distribution to capture correlation between spatial-spectral knowledge about different scenes, which can be generalized to unseen scenes. Extensive experiments on three benchmark datasets demonstrate that HyperEDL outperforms state-of-the-art methods, showcasing its effectiveness and strong generalization ability.