Evidential Deep Learning with Spectral-Spatial Uncertainty Disentanglement for Open-Set Hyperspectral Domain Generalization

• URL: http://arxiv.org/abs/2506.09460v1
• Date: Wed, 11 Jun 2025 07:07:14 GMT
• Title: Evidential Deep Learning with Spectral-Spatial Uncertainty Disentanglement for Open-Set Hyperspectral Domain Generalization
• Authors: Amirreza Khoshbakht, Erchan Aptoula,
• Abstract summary: We propose a novel open-set domain generalization framework for hyperspectral image classification.<n>The framework combines four key components: Spectrum-Invariant Frequency Disentanglement (SIFD), Dual-Channel Residual Network (DCRN), Evidential Deep Learning (EDL) and Spectral-Spatial Uncertainty Disentanglement (SSUD)<n> Experimental results show that our approach achieves performance comparable to state-of-the-art domain adaptation methods.
• Score: 5.770351255180493
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Open-set domain generalization(OSDG) for hyperspectral image classification presents significant challenges due to the presence of unknown classes in target domains and the need for models to generalize across multiple unseen domains without target-specific adaptation. Existing domain adaptation methods assume access to target domain data during training and fail to address the fundamental issue of domain shift when unknown classes are present, leading to negative transfer and reduced classification performance. To address these limitations, we propose a novel open-set domain generalization framework that combines four key components: Spectrum-Invariant Frequency Disentanglement (SIFD) for domain-agnostic feature extraction, Dual-Channel Residual Network (DCRN) for robust spectral-spatial feature learning, Evidential Deep Learning (EDL) for uncertainty quantification, and Spectral-Spatial Uncertainty Disentanglement (SSUD) for reliable open-set classification. The SIFD module extracts domain-invariant spectral features in the frequency domain through attention-weighted frequency analysis and domain-agnostic regularization, while DCRN captures complementary spectral and spatial information via parallel pathways with adaptive fusion. EDL provides principled uncertainty estimation using Dirichlet distributions, enabling the SSUD module to make reliable open-set decisions through uncertainty-aware pathway weighting and adaptive rejection thresholding. Experimental results on three cross-scene hyperspectral classification tasks show that our approach achieves performance comparable to state-of-the-art domain adaptation methods while requiring no access to the target domain during training. The implementation will be made available at https://github.com/amir-khb/SSUDOSDG upon acceptance.

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