Deep Feature Reconstruction Learning for Open-Set Classification of Remote-Sensing Imagery

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成果类型：

期刊论文

作者：

Sun, Hao;Li, Qianqian;Yu, Jie;Zhou, Dongbo;Chen, Wenjing;...

通讯作者：

Yu, J

作者机构：

[Sun, Hao] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Sch Comp, Wuhan 430079, Peoples R China.

[Sun, Hao] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China.

[Li, Qianqian; Zhou, Dongbo] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.

[Yu, Jie] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China.

[Yu, Jie] Wuhan Univ, Off Sci & Technol Dev, Wuhan 430072, Peoples R China.

通讯机构：

[Yu, J ] W

Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China.

语种：

英文

关键词：

Deep learning;feature reconstruction;open-set classification;remote-sensing imagery

期刊：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

ISSN：

1545-598X

年：

2023

卷：

页码：

1-5

DOI：

10.1109/LGRS.2023.3295871

基金类别：

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62201222, 62293553 and 62177017) 10.13039/501100003819-Hubei Provincial Natural Science Foundation of China (Grant Number: 2022CFB954) 10.13039/501100014219-National Science Fund for Distinguished Young Scholars (Grant Number: 61925112) 10.13039/501100012226-Fundamental Research Funds for the Central Universities (Grant Number: CCNU22QN014)

机构署名：

本校为第一机构

院系归属：

计算机学院

摘要：

Existing remote-sensing scene image (RSSI) classification methods usually rely on the static closed-set assumption that testing samples do not belong to unknown classes. However, practical applications are usually the open-set classification problem, which means that RSSIs from unknown classes will appear in the testing set. Most existing methods are prone to forcibly misclassify RSSIs of unknown classes into known classes, resulting in poor practical performance. In this letter, a deep feature reconstruction learning (DFRL) framework is proposed for the open-set classification of RSSIs (OSC-R...

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Deep Feature Reconstruction Learning for Open-Set Classification of Remote-Sensing Imagery

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