A COMPARATIVE ANALYSIS OF MUTUAL INFORMATION BASED FEATURE SELECTION FOR HYPERSPECTRAL IMAGE CLASSIFICATION

被引：0
|
作者：
Fu, Yuanyuan [1 ,2 ]
Jia, Xiuping [3 ]
Huang, Wenjiang [4 ]
Wang, Jihua [5 ]
机构：
[1] Zhejiang Univ, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Zhejiang, Peoples R China
[2] Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
[3] Univ New S Wales, Sch Engn & Informat Technol, Canberra, ACT 2600, Australia
[4] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
[5] Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Agr Stand & Testing, Beijing 100097, Peoples R China
来源：
2014 IEEE CHINA SUMMIT & INTERNATIONAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (CHINASIP) | 2014年
关键词：
feature selection; mutual information; classification; hyperspectral image; SUPPORT VECTOR MACHINES;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Feature selection is an important task for hyperspectral imagery classification and becomes more critical for the emerging big data analysis. Selection criteria based on mutual information theory have the advantages in terms of distribution free, nonlinearity and low computational load for multiclass cases. However several have been developed and are available to use. In this study, we conduct a comparative analysis on four defined criteria and their performances are evaluated using two hyperspectral data sets with two levels of sample sizes.
引用
收藏
页码：148 / 152
页数：5
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