H-FANet: A Hierarchical Multi-Scale Attention Network for Hyperspectral-LiDAR Land Cover Classification

Accurate land cover classification is critical for geographic information science. However, the fusion of hyperspectral and LiDAR data remains constrained by insufficient spectral-geometric coupling and limited scale representation. To address these challenges, we propose H-FANet, a hierarchical fusion attention network with a three-branch backbone (spectral, spatial, and elevation). Multi-scale enhancement modules are embedded in the spatial and elevation branches to capture scale-invariant features through hierarchical aggregation with convolutional splitting. For cross-modal interaction, H-FANet adopts a two-level fusion strategy: shape-level shallow cross-attention for geometric alignment and spectral-level deep residual fusion for semantic integration. Experiments on three benchmark datasets showed that H-FANet achieved overall accuracies of 99.13 ± 0.06% on Trento, 97.43 ± 0.06% on Houston 2013, and 92.11 ± 0.05% on the Muufl Gulfport datasets. The network outperformed five comparison methods by approximately 1.7–2.2% in overall accuracy. Ablation studies confirm the contributions of the multi-scale enhancement and hierarchical fusion modules. This GeoAI-driven framework improved land cover identification accuracy and could be applied to fields such as environmental monitoring, urban land use analysis, and ecological protection.