Semi-supervised hierarchical Transformer for hyperspectral Image classification

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成果类型：

期刊论文

作者：

He, Ziping;Zhu, Qianglin;Xia, Kewen;Ghamisi, Pedram;Zu, Baokai

通讯作者：

He, ZP

作者机构：

[He, Ziping; Zhu, Qianglin] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha, Peoples R China.

[Xia, Kewen] Hebei Univ Technol, Sch Elect & Informat Engn, Tianjin, Peoples R China.

[Ghamisi, Pedram] Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf HZDR, Freiberg, Germany.

[Ghamisi, Pedram] Inst Adv Res Artificial Intelligence IARAI, AI4RS artificial intelligence remote sensing, Vienna, Austria.

[Zu, Baokai] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China.

通讯机构：

[He, ZP ] C

Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410114, Peoples R China.

语种：

英文

关键词：

Hierarchical patch embedding;hyperspectral image classification;semi-supervised learning;transformer

期刊：

International Journal of Remote Sensing

ISSN：

0143-1161

年：

2024

卷：

期：

页码：

21-50

DOI：

10.1080/01431161.2023.2290996

基金类别：

Hebei Province Natural Science Foundation [E2021202179]; Key Research and Development Project from Hebei Province [19210404D, 20351802D, 21351803D]; General Project of Science and Technology of Beijing Municipal Education Commission [KM202210005023]

机构署名：

本校为第一且通讯机构

院系归属：

计算机与通信工程学院

摘要：

Transformer has achieved outstanding performance in many fields such as computer vision benefiting from its powerful and efficient modelling ability and long-range feature extraction capability complementary to convolution. However, on the one hand, the lack of CNN’s innate inductive biases, such as translation invariance and local sensitivity, makes Transformer require more data for learning. On the other hand, labelled hyperspectral samples are scarce due to the time-consuming and costly annotation task. To this end, we propose a semi-superv...

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Semi-supervised hierarchical Transformer for hyperspectral Image classification

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