[1]赵春晖,陈万海,万 建.一种改进的多类支持向量机超光谱图像分类方法[J].智能系统学报,2008,3(01):77-82.
 ZHAO Chun-hui,CHEN Wan-hai,WAN jian.An improved hyperspectral image classification method for a multiclass support vector machine[J].CAAI Transactions on Intelligent Systems,2008,3(01):77-82.
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一种改进的多类支持向量机超光谱图像分类方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第3卷
期数:
2008年01期
页码:
77-82
栏目:
出版日期:
2008-02-25

文章信息/Info

Title:
An improved hyperspectral image classification method for  a multiclass support vector machine
文章编号:
1673-4785(2008)01-0077-06
作者:
赵春晖陈万海万  建
哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001
Author(s):
ZHAO Chun-hui CHEN Wan-hai WAN jian
College of Information and Communication Engineering, Harbin Engineering Univer sity, Harbin 150001,China
关键词:
支持向量机二次分类多类支持向量机
Keywords:
support vector machine secondary classification multiclass SVM
分类号:
TN919.81
文献标志码:
A
摘要:
支持向量机(SVM)是建立在统计学理论基础上的一种机器学习方法,用于解决二类分类问题,如何有效地将其推广到多类分类问题是一个正在研究的课题.总结了现有的主要的支持向量机多类分类算法,并在1a1 SVM分类算法基础上提出一种二次分类的方法 . 改良了惩罚因子,提高了不易分的类别之间的可分程度.通过对超光谱图像进行分类实验,结果表明该方法具有较高的分类精度.
Abstract:
SVM is a machine learning method developed on the basis of statistics theory and originally designed for binary classification problems. The most effe ctive way to extend it for multiclass classification is still an area of conside rable discussion. This paper presental a secondary classification method based o n 1a1 SVM classification algorithm after a general overview of typical method s for a multiclass SVM. Our method improves the penalty factors, so it enhances th e divisibility of classes that were difficult to classify. Experimental results o f hyperspectral image classification showed that the suggested multiclass SVM ha s higher classification precision.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2007-06-06.
基金项目:
高等学校博士学科点基金资助项目(20060217021);
黑龙江省自然科学基金资助项目(ZJG060601).
作者简介:
赵春晖,男,1965年生,教授,博士生导师.获省部级科技奖5项.主要研究方向为智能信息处理技术、图像处理.出版著作3部,发表论文200余篇.
 陈万海,男,1963年生,副教授,博士研究生.主要研究方向为超光谱遥感图像处理技术,发表论文18篇.  
万 建,男,1980年生,博士研究生.主要研究方向为信号与图像处理,发表论文5篇.
通讯作者:赵春晖.E-mail:zhaochunhui@hrbeu.edu.cn.
更新日期/Last Update: 2009-05-10