Hyperspectral and LiDAR data classification based on linear self-attention

TL;DR

This paper introduces a linear self-attention fusion model for joint classification of hyperspectral and LiDAR data, achieving high accuracy and demonstrating superiority over existing methods.

Contribution

The paper proposes a novel linear self-attention fusion model with a plug-and-play attention module for hyperspectral and LiDAR data classification.

Findings

Achieved 95.40% accuracy on Houston dataset

Outperformed state-of-the-art models in classification tasks

Demonstrated versatility of the attention module

Abstract

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.