Orientational Clustering Learning for Open-Set Hyperspectral Image Classification

首页 > 成果 > 详情

认领

导出

Link by DOI

反馈

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：

Xu, Hao;Chen, Wenjing;Tan, Cheng;Ning, Hailong;Sun, Hao*;...

通讯作者：

Sun, Hao;Xie, W

作者机构：

[Xu, Hao; Tan, Cheng; Xie, W; Xie, Wei; Sun, Hao; Sun, H] Cent China Normal Univ, Sch Comp Sci, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.

[Xu, Hao; Tan, Cheng; Xie, W; Xie, Wei; Sun, Hao; Sun, H] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Peoples R China.

[Chen, Wenjing] Hubei Univ Technol, Sch Comp Sci, Wuhan 430068, Peoples R China.

[Ning, Hailong] Xian Univ Posts & Telecommun, Sch Comp Sci & Technol, Xian 710121, Peoples R China.

通讯机构：

[Xie, W ; Sun, H] C

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

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

语种：

英文

关键词：

Feature extraction;Hyperspectral imaging;Training;Vectors;Graphical models;Distribution functions;Sun;Class anchors;hyperspectral image (HSI);open-set classification;orientational clustering learning (OCL)

期刊：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

ISSN：

1545-598X

年：

2024

卷：

DOI：

10.1109/LGRS.2024.3432604

基金类别：

National Natural Science Foundation of China [62201222, 62377026]; Knowledge Innovation Program of Wuhan-Shuguang Project [2023010201020382, 2023010201020377]; Self-Determined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE [CCNU22QN014, CCNU24ai011, CCNU24JCPT031]

机构署名：

本校为第一且通讯机构

院系归属：

计算机学院

摘要：

Recently, some literature has begun to pay attention to the open-set problem in remote sensing application scenarios and studied various open-set hyperspectral image classification (OSHIC) methods. These OSHIC methods are usually based on deep neural networks, using the nondirectional Euclidean distance losses to constrain latent sample representations of known classes to be compact. Nonetheless, the potential effect of the spatial distribution of sample representations is ignored, resulting in degraded classification performance in OSHIC. In this letter, we propose an orientational clustering...

反馈

产权有误：本人成果被他人认领

数据有误：数据基本信息有误

归属有误：成果的院系归属、机构署名归属有误

其他原因：

验证码：

看不清楚，换一个

确定

取消

成果认领

标题：

用户	作者	通讯作者	--
	请选择	请选择	--

确定

取消

Orientational Clustering Learning for Open-Set Hyperspectral Image Classification

反馈

成果认领

提示

该栏目需要登录且有访问权限才可以访问