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