摘要： 高光谱图像（HSI）与激光雷达（LiDAR）图像的联合分类能够充分发挥两者在光谱与空间结构信息方面的互补优势，已成为遥感领域的重要研究方向。然而，由于两种数据来源的成像机制存在显著差异，HSI与LiDAR在数据维度构成和特征分布上表现出高度异构性，这对多模态数据的语义表征与高效融合带来了严峻挑战。为应对上述挑战，提出了一种用于联合HSI和LiDAR数据分类的多尺度混合卷积Mamba网络（MHCMNet）。该框架首先通过多尺度特征提取模块（MFEM），从两种数据中分别提取光谱、空间和高程特征；随后，利用并行特征标记化模块（FTM）将两种模态的特征转换为统一的特征标记。为进一步增强多模态特征的协同表达能力，MHCMNet创新性地引入了基于Mamba架构的特征融合模块（MFFM），借助其出色的长程依赖建模能力，实现模态内及模态间特征的深度关联与高效融合。实验结果表明，MHCMNet在Trento、Houston2013和MUUFL三个数据集上分别取得了99.03%、90.71%和91.47%的最高总体精度（OA），同时保持了较低的模型复杂度。进一步的消融实验验证了各模块在性能提升中的有效性，充分证明了所提方法在多源遥感数据分类中的优越性能。

Abstract: The joint classification of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data can fully leverage their complementary advantages in spectral and spatial-structural information, and has become an important research focus in the field of remote sensing. However, due to significant differences in their imaging mechanisms, HSI and LiDAR exhibit a high degree of heterogeneity in terms of data dimensionality and feature distribution, which poses severe challenges for semantic representation and efficient fusion of multimodal data. To address these challenges, we propose a Multi-Scale Hybrid Convolution Mamba Network (MHCMNet) for joint HSI and LiDAR data classification. Specifically, the framework first employs a Multi-Scale Feature Extraction Module (MFEM) to extract spectral, spatial, and elevation features from the two modalities. Subsequently, the parallel Feature Tokenization Module (FTM) transforms the features of both modalities into unified feature tokens. To further enhance the collaborative representation of multimodal features, MHCMNet innovatively introduces a Mamba-based Feature Fusion Module (MFFM), which leverages its powerful long-range dependency modeling capability to achieve deep intra- and inter-modal feature interaction and efficient fusion. Experimental results demonstrate that MHCMNet achieves the highest overall accuracy (OA) of 99.03%, 90.71%, and 91.47% on the Trento, Houston2013, and MUUFL datasets, respectively, while maintaining low model complexity. In addition, ablation studies validate the effectiveness of each module in performance improvement, further confirming the superiority of the proposed method in multi-source remote sensing data classification.