Multiple-feature latent space learning-based hyperspectral image classification

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Zhao, Yue;Peng, Jiangtao;Wei, Yantao;Peng, Qinmu;Mou, Yi

通讯作者：

Peng, J.

作者机构：

[Zhao, Yue] Hubei Univ, Sch Comp Sci & Informat Engn, Wuhan 430062, Peoples R China.

[Peng, Jiangtao] Hubei Univ, Fac Math & Stat, Hubei Key Lab Appl Math, Wuhan 430062, Peoples R China.

[Wei, Yantao] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.

[Peng, Qinmu] Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Peoples R China.

[Mou, Yi] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.

通讯机构：

[Peng, J.] H

Hubei Key Laboratory of Applied Mathematics, China

语种：

英文

关键词：

Feature extraction;Training;Hyperspectral imaging;Learning systems;Computational complexity;Electronic mail;Hyperspectral image (HSI) classification;multiple-feature latent space (MFLS) learning;spatial information

期刊：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

ISSN：

1545-598X

年：

2021

卷：

期：

页码：

1836-1840

DOI：

10.1109/LGRS.2020.3008847

基金类别：

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61871177 and 61772220) 10.13039/501100012226-Fundamental Research Funds for the Central Universities (Grant Number: 2018KFYYXJJ135) SZSTI (Grant Number: JCYJ20180305180637611 and JCYJ20180305180804836) 10.13039/100012554-Chutian Scholar Program-Chutian Student of Hubei Province through the Project of Hubei Provincial Department of Education (Grant Number: B2019064) 10.13039/501100003819-Natural Science Foundation of Hubei Province (Grant Number: 2018CFB691)

机构署名：

本校为其他机构

院系归属：

教育信息技术学院

摘要：

Considering that multiple features can improve the classification performance as they contain diversity information of images, a multiple-feature latent space learning-based method is proposed for hyperspectral image (HSI) classification in this letter. In the proposed method, a latent space that contains diversity information of multiple features and transformation matrices between the latent space and features are both learned. Moreover, spatial information is used for labeling unlabeled samples in the classification. Experimental results on the Indian Pines and University of Pavia data sets...

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Multiple-feature latent space learning-based hyperspectral image classification

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