Cross-SPECL: Cross-Scene Hyperspectral Image Classification via Spectral Stability Exploiting and Causal Learning

Single-source domain generalization (DG) presents a formidable challenge in cross-scene hyperspectral image (HSI) classification, as models trained on a single domain often struggle to generalize due to spectral shifts. Prevailing approaches predominantly concentrate on expanding the source distribution through data augmentation or adversarial training techniques. However, such strategies often neglect a critical pitfall: in the absence of explicit constraints, models are prone to overfitting domain-specific spurious correlations rather than capturing intrinsic invariant semantics. Moreover, the reliance on adversarial training frequently introduces instability and convergence difficulties. To address these limitations, this paper proposes Cross-SPECL, a unified non-adversarial framework that transitions the paradigm to a structured “Stabilize-then-Disentangle” process. Specifically, the proposed framework first introduces a Spectral Patch Low-Frequency Transformation Network as a feature stabilizer. This module utilizes a dual-branch architecture wherein an auxiliary branch applies controlled perturbations to low-frequency components, thereby compelling the main branch to extract features resilient to style variations. To further consolidate this robustness, a Synergistic Feature Stabilization mechanism integrates Supervised and Prototype Contrastive Learning to enforce invariance to these perturbations and construct highly representative class-conditional prototypes. Building upon this foundation, the framework learns a Domain-Agnostic Directed Acyclic Graph (DAG) derived from these prototypes to explicitly uncover the causal structure. This process generates a causal mask that effectively prunes domain-specific spurious features, ensuring robust generalization. Extensive experimental evaluations on three cross-scene HSI classification benchmarks demonstrate that Cross-SPECL yields significant performance improvements over state-of-the-art methods.