VDCnet: Calibrated Domain Expansion and View Semantic Matching for Cross-Scene HSI Classification

Abstract

Cross-scene hyperspectral image (HSI) classification seeks to learn a classifier from annotated source-scene data and deploy it on unlabeled scenes whose imaging conditions and data distributions differ from those seen during training. Existing cross-scene learning strategies mainly include domain adaptation (DA) and domain generalization (DG). In practical scenarios, target-domain samples are commonly unknown, inaccessible, or time-varying before deployment. DG is a more practical choice for such applications. Yet, single-source DG still faces a key difficulty: expanded samples must contain meaningful domain changes without corrupting class semantics. If the generated domains are weak or deviate from their original categories, the classifier may learn unstable or misleading cues. Therefore, we introduce the View-Consistent Domain Calibration Network (VDCnet), which is designed to improve the quality and training value of generated samples for single-source cross-scene classification. VDCnet consists of a Calibrated Expansion Generator (CEG) and a View Semantic Matching Mechanism (VSM). CEG performs reliability-gated spectral-spatial residual perturbation to produce semantically trustworthy extended samples, rather than simply enlarging the sample set. VSM further enforces multi-view semantic consistency in both prediction distributions and projected feature representations, promoting diverse feature learning while suppressing semantic drift. Experiments on the Houston, Pavia, and Shanghai–Hangzhou datasets demonstrate that VDCnet improves overall accuracy over the leading DG methods by 1.29, 2.65, and 1.26 percentage points, respectively, indicating its superior performance.