AI RESEARCH
Spectral Property-Driven Data Augmentation for Hyperspectral Single-Source Domain Generalization
arXiv CS.CV
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ArXi:2603.16662v1 Announce Type: new While hyperspectral images (HSI) benefit from numerous spectral channels that provide rich information for classification, the increased dimensionality and sensor variability make them sensitive to distributional discrepancies across domains, which in turn can affect classification performance. To tackle this issue, hyperspectral single-source domain generalization (SDG) typically employs data augmentation to simulate potential domain shifts and enhance model robustness under the condition of single-source domain.