发表机构
Aerospace Information Research Institute, Chinese Academy of Sciences; Key Laboratory of Target Cognition and Application Technology; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences; State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences; Helmholtz-Zentrum Dresden-Rossendorf; Faculty of Electrical and Computer Engineering, University of Iceland（中国科学院空天信息创新研究院; 目标认知与应用技术重点实验室; 中国科学院大学电子电气与通信工程学院; 中国科学院空天信息创新研究院遥感与数字地球国家重点实验室; 亥姆霍兹德累斯顿罗森多夫研究中心; 冰岛大学电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高光谱与LiDAR融合中长距离依赖和可解释性不足的问题，提出基于热传导的M2Heat框架，结合vHeat模块和跨频率融合，在三个基准上实现高效且可解释的分类。
AI 中文摘要
高光谱（HS）与光探测和测距（LiDAR）数据的融合，通过联合利用光谱、空间和结构线索，在提升土地覆盖分类方面发挥着关键作用。然而，现有的多模态融合方法在保持计算效率的同时，仍难以建模长距离依赖和复杂的各向异性交互。本文提出了M2Heat，一个受物理启发的框架，通过热传导的视角研究多模态融合。其核心是一个物理驱动的视觉热传导模块（vHeat）和增强的频率值嵌入（FVEs），模拟各向异性信息流，从而以亚二次复杂度和物理可解释性捕获全局依赖。该机制与名为跨频率融合（CFF）模块的混合空间-频率融合策略相结合，产生高度判别性和鲁棒性的特征表示。M2Heat在三个基准数据集（即Trento、Houston2013和Augsburg）上取得了具有竞争力的整体性能，同时为多模态特征融合提供了可解释的热传导引导视角。这些结果表明，热传导引导的神经算子有望实现高效且可解释的遥感多模态融合。源代码公开于https://github.com/Weikan0425/M2Heat_HSI_LiDAR。
英文摘要
The fusion of hyperspectral (HS) and Light Detection and Ranging (LiDAR) data plays a crucial role in enhancing land-cover classification by jointly exploiting spectral, spatial, and structural cues. However, existing multimodal fusion methods still struggle to model long-range dependencies and complex anisotropic interactions while maintaining computational efficiency. This paper introduces M2Heat, a physics-inspired framework that investigates multimodal fusion through the lens of heat conduction. At its core, a physics-driven visual heat conduction module (vHeat) and enhanced Frequency Value Embeddings (FVEs) simulate anisotropic information flow, enabling the capture of global dependencies with sub-quadratic complexity and physical interpretability. This mechanism, combined with a hybrid spatial-frequency fusion strategy named Cross-Frequency Fusion (CFF) module, produces highly discriminative and robust feature representations. M2Heat achieves competitive overall performance on three benchmarks, i.e., Trento, Houston2013, and Augsburg, while providing an interpretable heat-conduction-guided perspective for multimodal feature fusion. These results indicate the potential of heat-conduction-guided neural operators for efficient and interpretable RS multimodal fusion. The source code is publicly available at https: /github.com/Weikan0425/M2Heat_HSI_LiDAR.
CommentsAccepted by IEEE TCSVT
