Аннотация
Local Climate Zone (LCZ) classification provides a standardized framework for characterizing urban morphology and its climatic implications. However, most existing remote sensing-based LCZ mapping methods rely on pixel-level classification and multispectral data alone, which limits their ability to capture urban scene heterogeneity and to distinguish structurally similar LCZ classes. In this paper, we propose LCZ-HMSSNet, a deep learning framework for scene-level LCZ classification that integrates PRISMA hyperspectral images with Sentinel-2 multispectral data. The proposed approach exploits both the spectral richness of hyperspectral data and the spatial context provided by multispectral observations, and incorporates a spatial–spectral feature separation mechanism to enhance the discriminability of the fused representations. Experiments conducted across six representative European cities evaluate the proposed method from multiple perspectives, including comparisons with different classification models, data contribution analysis, and structural ablation studies. The results demonstrate that the proposed method consistently outperforms MSI-only and existing LCZ classification approaches, achieving an overall accuracy (OA) of 0.988 and a Kappa of 0.985. In addition, the small-sample experiments indicate the robustness and potential of the proposed model, providing a practical reference for future LCZ mapping in data-scarce scenarios.