Multicluster Spatial-Spectral Unsupervised Feature Selection for Hyperspectral Image Classification

被引：7

作者：

Li, Haichang

Xiang, Shiming ^{[1
]}

Zhong, Zisha ^{[1
]}

Ding, Kun ^{[1
]}

Pan, Chunhong ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2015年 / 12卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Clustering; feature selection; hyperspectral; spatial-spectral; BAND SELECTION; REGRESSION;

D O I：

10.1109/LGRS.2015.2418232

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

A new unsupervised spatial-spectral feature selection method for hyperspectral images has been proposed in this letter. The key idea is to select the features that better preserve the multicluster structure of the multiple spatial-spectral features. Specifically, the multicluster structure information is obtained through spectral clustering utilizing a weighted combination of the multiple features. Then, such information is preserved in a group-sparsity- based robust linear regression model. The features that contribute more in preserving the multicluster structure information are selected. Comparative experiments on two popular real hyperspectral images validate the effectiveness of the proposed method, showing higher classification accuracy.

引用

页码：1660 / 1664

页数：5

共 23 条

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Cai D., 2010, P 16 ACM SIGKDD INT, P333, DOI DOI 10.1145/1835804.1835848

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