Multi-modal land cover classification plays an important role in remote sensing applications such as urban monitoring and environmental analysis. By integrating complementary information from hyperspectral imagery (HSI) and LiDAR data, multimodal learning can significantly improve classification performance. However, existing Transformer-based fusion methods often suffer from high computational complexity and inefficient cross-modal interaction modeling, which limits their applicability in resource-constrained scenarios. To address these challenges, we propose LMFusion, an efficient framework for multimodal feature learning. Specifically, LMFusion enables efficient bidirectional feature interaction through a linear-complexity cross-attention mechanism and enhances long-range spatial-spectral representation learning with Mamba-based state space modeling, thereby achieving effective multimodal dependency modeling with linear computational complexity. In addition, a selective quantization-aware optimization strategy is introduced to support multiple bit-width settings (down to 1-bit), yielding a more compact and efficient model while improving representation robustness under low-bit constraints. Extensive experiments on the Houston2013, MUUFL, and Augsburg datasets demonstrate the effectiveness of LMFusion. It achieves overall accuracies of 95.84%, 94.95%, and 99.05%, respectively, consistently outperforming representative multimodal classification methods and showing strong potential for accurate and efficient multimodal remote sensing classification.
Zhou et al. (Sat,) studied this question.