Hyperspectral and LiDAR data classification based on linear self-attention

Min Feng; Feng Gao; Jian Fang; Junyu Dong

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arXiv · 2104.02301

Hyperspectral and LiDAR data classification based on linear self-attention

Min Feng·Feng Gao·Jian Fang·Junyu Dong

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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.

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BibTeX RIS

Min Feng, Feng Gao, Jian Fang, Junyu Dong. 2021-04-06. Hyperspectral and LiDAR data classification based on linear self-attention. https://arxiv.org/abs/2104.02301

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