Comparison of some feature subset selection methods for use in remote sensing image analysis

被引：0

作者：

Smits, PC ^{[1
]}

机构：

[1] Space Applicat Inst, Environm & Geoinformat Unit, Joint Res Ctr, I-21020 Ispra, VA, Italy

来源：

IGARSS 2001: SCANNING THE PRESENT AND RESOLVING THE FUTURE, VOLS 1-7, PROCEEDINGS | 2001年

关键词：

remote sensing image analysis; hyperspectral image analysis; pattern recognition; feature subset selection;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

As feature subset selection constitutes an important aspect of data fusion in general, this paper compares different measures of goodness and their influence on the classification results. These measures are 1) Fukunaga's criterion [5], and 2) the ML criterion with a user-specified upper limit for the total error [12]. Results are presented using publicly available multi-spectral/multi-sensor and hyper-spectral images, and it is concluded that the ML criterion with a user-specified upper limit for the total error is a valid alternative to classical methods.

引用

页码：530 / 533

页数：4

共 12 条

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