Context-Aware Joint Alignment for Cross-Scene Hyperspectral Image Classification

Cross-scene hyperspectral image (HSI) classification facespersistent challenges due to domain shifts in spectral signatures, spatialstructures, and semantic compositions. Existing domain generalizationmethods typically align either features or predictions, but they overlookhow local context (e.g., boundary mixing and class co-occurrence pat-terns) changes across scenes. As a result, one-sided alignment often yieldspartial adaptation: feature-invariant models may still produce unstableoutputs, while output-aligned models can rely on domain-specific short-cuts. We propose Context-Aware Joint Alignment (CAJA), a pixel-faithful framework that preserves the single-label objective while inject-ing explicit context into training. CAJA combines Context-Aware Su-pervision (CAS) to improve boundary-aware supervision using neigh-borhood context and homogeneity, and Context-Conditioned JointAlignment (CCJA) to enforce context-conditioned alignment in bothfeature and prediction spaces across domains. By coupling supervisionand joint alignment under shared context, CAJA mitigates the partial-alignment limitation of one-sided methods. Extensive experiments onthree cross-scene HSI benchmarks show that CAJA consistently improvesgeneralization, especially at class boundaries where context shift is mostsevere, establishing a simple, principled, and deployment-friendly solu-tion for cross-scene HSI domain generalization.