H-FANet: A Hierarchical Multi-Scale Attention Network for Hyperspectral-LiDAR Land Cover Classification
TL;DR
H-FANet, a hierarchical fusion attention network with a three-branch backbone, improved land cover identification accuracy and could be applied to fields such as environmental monitoring, urban land use analysis, and ecological protection.
Abstract
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.