HYPERSPECTRAL AND LIDAR DATA CLASSIFICATION BASED ON LINEAR SELF-ATTENTION

被引：12
作者：
Feng, Min [1 ,2 ]
Gao, Feng [1 ,2 ]
Fang, Jian [1 ,2 ]
Dong, Junyu [1 ,2 ]
机构：
[1] Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Peoples R China
[2] Ocean Univ China, Inst Marine Dev, Qingdao, Peoples R China
来源：
2021 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IGARSS | 2021年
基金：
中国国家自然科学基金;
关键词：
hyperspectral image; LiDAR; cross-modal data fusion; classification; FUSION;
D O I：
10.1109/IGARSS47720.2021.9553769
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
In this paper, an efficient linear self-attention fusion model is proposed 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.
引用
收藏
页码：2401 / 2404
页数：4
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