MUFNet: A Mamba-Based Uncertainty-Aware Fusion Framework for Fine-Grained Satellite Hyperspectral-LiDAR Tree Species Mapping

Satellite hyperspectral imagery (HSI) fused with LiDAR enables large-area, fine-grained tree-species mapping in subtropical forests. However, current satellite HSI-LiDAR fusion remains limited because non-task-oriented spectral reduction may weaken discriminative narrowband and inter-band cues, while weakly coupled fusion cannot adequately address cross-modal inconsistency in satellite observations. To address these issues, we propose MUFNet, a mamba-based uncertainty-aware fusion framework that integrates multi-stage cross-modal interactions with uncertainty-guided prediction aggregation under end-to-end task-driven representation learning. We validate MUFNet in a representative subtropical region and benchmark it against six state-of-the-art (SOTA) models. Experimental results demonstrate that MUFNet achieves the best mapping performance, with an overall accuracy (kappa) of 93.34% (91.17%). Moreover, MUFNet significantly outperforms all competing methods, improving overall accuracy (kappa) by 2.85%–11.45% (6.45%–15.49%). In addition, MUFNet exhibits good predictive reliability, achieving an ECE of 0.0073, and maintains a favorable performance–efficiency trade-off with only 0.400 M parameters and 0.086 GFLOPs. Ablation studies further confirm the complementarity of the three fusion stages and the contributions of the proposed task-driven representation learning, cross-modal interaction, and uncertainty-aware routing components. This work provides a transferable technical pathway for fine-grained tree species mapping under multi-source remote sensing, offering methodological support for biodiversity conservation and forest resource management.