版权说明 操作指南
首页 > 成果 > 详情

Pairwise Elastic Net Representation-Based Classification for Hyperspectral Image Classification

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Li, Hao;Zhang, Yuanshu;Ma, Yong;Mei, Xiaoguang;Zeng, Shan;...
通讯作者:
Ma, Y.
作者机构:
[Li, Yaqin; Li, Hao; Zeng, Shan] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
[Mei, Xiaoguang; Zhang, Yuanshu; Ma, Yong] Wuhan Univ, Elect Informat Sch, Wuhan 430072, Peoples R China.
通讯机构:
[Ma, Y.] E
Electronic Information School, China
语种:
英文
关键词:
Collaborative representation;Hyperspectral image (HSI) classification;Neighbor information;Pairwise elastic net;Sparse representation
期刊:
Entropy
ISSN:
1099-4300
年:
2021
卷:
23
期:
8
页码:
956
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61903279, 61773295]; NSFCNational Natural Science Foundation of China (NSFC) [61906140]; NSFC-CAAC [U1833119]; Hubei Natural Science Foundation for Distinguished Young Scholars [2020CFA063]; National Food and Strategic Reserves Administration Foundation [LQ2018501]
机构署名:
本校为第一机构
院系归属:
数学与计算机学院
摘要:
The representation-based algorithm has raised a great interest in hyperspectral image (HSI) classification. l1-minimization-based sparse representation (SR) attempts to select a few atoms and cannot fully reflect within-class information, while l2-minimization-based collaborative representation (CR) tries to use all of the atoms leading to mixed-class information. Considering the above problems, we propose the pairwise elastic net representation-based classification (PENRC) method. PENRC combines the l1-norm and l2-norm penalties and introduces...

反馈

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

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com