Hyperspectral and LiDAR data classification based on linear self-attention
This work addresses classification challenges in remote sensing for applications like environmental monitoring, but it is incremental as it builds on existing attention-based methods.
The paper tackled hyperspectral and LiDAR data joint classification by proposing an efficient linear self-attention fusion model, achieving an overall accuracy of 95.40% on the Houston dataset.
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.