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An efficient linear self-attention fusion model is proposed in this paper forthe task of hyperspectral image (HSI) and LiDAR data joint classification. Theproposed method is comprised of a feature extraction module, an attentionmodule, and a fusion module. The attention module is a plug-and-play linearself-attention module that can be extensively used in any model. The proposedmodel has achieved the overall accuracy of 95.40\% on the Houston dataset. Theexperimental results demonstrate the superiority of the proposed method overother state-of-the-art models.

Hyperspectral and LiDAR data classification based on linear self-attention