KHLDGNet: Knowledge-Driven Hierarchical Language-Aware Domain Generalization Network for Cross-Scene Wetland Hyperspectral Image Classification

Abstract

Wetlands are fragile yet vital ecosystems that require precise remote sensing (RS) mapping. Hyperspectral image (HSI) provides high spectral resolution for fine-scale wetland classification, but cross-scene application suffers from domain shifts and a lack of model interpretability. To address these issues, this article proposes a knowledge-driven hierarchical language-aware domain generalization network (KHLDGNet) for interpretable cross-scene wetland HSI classification. Unlike existing vision–language methods that merely use category names or simple attributes as auxiliary text, the core innovation of our framework is the construction of a structured, four-level geographic knowledge hierarchy that encompasses conceptual, rule, instance, and causal. This hierarchy transforms a geographical cognitive framework into computable semantic constraints, guiding the model from superficial pattern matching to deep mechanistic understanding. A MixStyle module in the visual encoder implicitly diversifies feature-level styles to counter spectral and textural variations. Visual and textual features are aligned in a shared semantic space via supervised contrastive learning, unifying generalization and interpretability. Experiments on three real-world wetland datasets demonstrate that KHLDGNet significantly outperforms state-of-the-art domain generalization (DG) methods. Visualization and case studies confirm its semantic explanation capability, contributing to explainable geographic AI (XGeoAI). Code available at https://github.com/SYFYN0317/KHLDGNet