The fusion of hyperspectral and light detection and range (LiDAR) data has been an active research topic. Existing fusion methods have ignored the high-dimensionality and redundancy challenges in hyperspectral images (HSIs), despite that band selection methods have been intensively studied for HSI processing. This article 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 datasets: 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.

中文翻译：

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LiDAR 引导的交叉注意融合用于高光谱波段选择和图像分类

高光谱和光探测与测距（LiDAR）数据的融合一直是一个活跃的研究课题。尽管针对 HSI 处理的波段选择方法已得到深入研究，但现有的融合方法忽略了高光谱图像 (HSI) 中的高维性和冗余挑战。本文通过从 Transformer 架构中引入交叉注意机制来选择由 LiDAR 数据引导的 HSI 频段，从而解决了这一重大差距。激光雷达提供高分辨率垂直结构信息，可用于区分可能具有相似光谱特征但不同结构轮廓的不同类型的土地覆盖。在我们的方法中，LiDAR 数据被用作“查询”，从 HSI 中搜索和识别“密钥”，从而为 LiDAR 选择最相关的频段。该方法可确保所选 HSI 频段大幅减少冗余和计算要求，同时以最佳方式处理 LiDAR 数据。我们在三对 HSI 和 LiDAR 数据集上进行了广泛的实验：Houston 2013、Trento 和 MUUFL。结果凸显了交叉注意机制的优越性，强调了与 LiDAR 特征融合时识别的 HSI 波段的分类准确性得到了提高。结果还表明，使用更少的频段与激光雷达相结合超越了最先进的融合模型的性能。