Feature selection and classification of hyperspectral images, with support vector machines

被引:207
作者
Archibald, Rick [1 ]
Fann, George [1 ]
机构
[1] Oak Ridge Natl Lab, Div Math & Comp Sci, Oak Ridge, TN 37831 USA
关键词
feature selection; hyperspectral images; support vector machines (SVMs);
D O I
10.1109/LGRS.2007.905116
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
Hyperspectral images consist of large number of bands which require sophisticated analysis to extract. One approach to reduce computational cost, information representation, and accelerate knowledge discovery is to eliminate bands that do not add value to the classification and analysis method which is being applied. In particular, algorithms that perform band elimination should be designed to take advantage of the structure of the classification method used. This letter introduces an embeddedfeature-selection (EFS) algorithm that is tailored to operate with support vector machines (SVMs) to perform band selection and classification simultaneously. We have successfully applied this algorithm to determine a reasonable subset of bands with- out any user-defined stopping criteria on some sample AVIRIS images; a problem occurs in benchmarking recursive-feature-elimination methods for the SVMs.
引用
收藏
页码:674 / 677
页数:4
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