The fusion of hyperspectral and LiDAR data has been an active research topic. Existing fusion methods have ignored the high-dimensionality and redundancy challenges in hyperspectral images, despite that band selection methods have been intensively studied for hyperspectral image (HSI) processing. This paper addresses this significant gap by introducing a cross-attention mechanism from the transformer architecture for the selection of HSI bands guided by LiDAR data. LiDAR provides high-resolution vertical structural information, which can be useful in distinguishing different types of land cover that may have similar spectral signatures but different structural profiles. In our approach, the LiDAR data are used as the "query" to search and identify the "key" from the HSI to choose the most pertinent bands for LiDAR. This method ensures that the selected HSI bands drastically reduce redundancy and computational requirements while working optimally with the LiDAR data. Extensive experiments have been undertaken on three paired HSI and LiDAR data sets: Houston 2013, Trento and MUUFL. The results highlight the superiority of the cross-attention mechanism, underlining the enhanced classification accuracy of the identified HSI bands when fused with the LiDAR features. The results also show that the use of fewer bands combined with LiDAR surpasses the performance of state-of-the-art fusion models.

翻译：高光谱与LiDAR数据的融合一直是活跃的研究课题。尽管波段选择方法在高光谱图像处理中得到了深入研究，但现有融合方法忽视了高光谱图像中的高维性与冗余性挑战。本文通过引入基于Transformer架构的跨注意力机制，以LiDAR数据为指导进行高光谱波段选择，填补了这一重要空白。LiDAR可提供高分辨率的垂直结构信息，有助于区分光谱特征相似但结构轮廓不同的各类地表覆盖物。在本方法中，LiDAR数据作为"查询"来搜索并识别高光谱数据中的"键"，从而选出与LiDAR最相关的波段。该方法确保所选高光谱波段在显著降低冗余度和计算需求的同时，能够与LiDAR数据实现最优协同。我们在三组配对的HSI与LiDAR数据集（Houston 2013、Trento和MUUFL）上开展了大量实验。结果凸显了跨注意力机制的优越性，表明所选高光谱波段在与LiDAR特征融合时可显著提升分类精度。此外，实验还显示，使用更少波段与LiDAR结合即可超越现有最优融合模型的性能。