[1]刘振林,谷延锋,张晔.一种用于高光谱图像特征提取的子空间核方法[J].哈尔滨工程大学学报,2014,(02):238-244.[doi:10.3969/j.issn.10067043.201309025]
 LIU Zhenlin,GU Yanfeng,ZHANG Ye.A subspace kernel learning method for feature extraction of the hyperspectral image[J].hebgcdxxb,2014,(02):238-244.[doi:10.3969/j.issn.10067043.201309025]
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一种用于高光谱图像特征提取的子空间核方法(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2014年02期
页码:
238-244
栏目:
出版日期:
2014-02-25

文章信息/Info

Title:
A subspace kernel learning method for feature extraction of the hyperspectral image
文章编号:
10067043(2014)02023807
作者:
刘振林 谷延锋 张晔
哈尔滨工业大学 电子与信息工程学院,黑龙江 哈尔滨 150001
Author(s):
LIU Zhenlin GU Yanfeng ZHANG Ye
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
关键词:
高光谱图像核方法数据降维图像分类特征提取
分类号:
TN911.73
DOI:
10.3969/j.issn.10067043.201309025
文献标志码:
A
摘要:
特征提取对于实现高光谱遥感图像的有效信息挖掘和利用以及提高后续分类应用有着重要价值。为了改进降维效果,提出一种子空间调制的核主成分分析方法,将高光谱数据分组特性整合到一个统一的核方法框架中,并构造子空间调制核。子空间调制核依靠特征分组实现了在光谱波段上的稀疏调制,它也是一个数据自适应的核,用于度量高光谱数据样本间的非线性相似性。该方法利用AVIRIS真实高光谱图像进行评估,并且与传统的核方法、光谱加权核方法进行了比较。实验结果表明,基于子空间调制的核方法更充分地利用了波段间复杂相关的物理特性,进而在高光谱图像分类方面的结果好于传统的核方法与光谱加权核方法。

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2013-09-06. 网络出版时间:2014-1-2 15:26:13. 基金项目:国家自然科学基金资助项目(61371180). 作者简介:刘振林(1970-), 男, 博士研究生; 谷延锋(1977-), 男, 教授,博士生导师. 通信作者:谷延锋, E-mail: guyf@hit.edu.cn.
更新日期/Last Update: 2014-04-23