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：高光谱与激光雷达数据的融合一直是活跃的研究课题。现有融合方法忽视了高光谱图像中的高维度和冗余性挑战，尽管波段选择方法在高光谱图像处理中已被广泛研究。本文通过引入Transformer架构中的交叉注意力机制，由激光雷达数据引导进行高光谱图像波段选择，填补了这一重要空白。激光雷达提供高分辨率垂直结构信息，有助于区分光谱特征相似但结构轮廓不同的地物类型。在我们的方法中，激光雷达数据作为"查询"来搜索并识别高光谱图像中的"键"，从而选择与激光雷达最相关的波段。该方法确保所选高光谱波段显著降低冗余性和计算需求，同时与激光雷达数据实现最优协同。我们在三组配对的高光谱与激光雷达数据集（Houston 2013、Trento和MUUFL）上进行了广泛实验。结果突显了交叉注意力机制的优越性，表明所选高光谱波段与激光雷达特征融合后分类精度得到提升。实验结果还显示，使用更少波段结合激光雷达的性能超越了现有最先进的融合模型。