摘要
近年来,随着遥感技术的飞速发展,遥感影像的处理和分类已成为遥感应用研究中的一个热点,粗糙集(RS)理论和支持向量机(SVM)在信息处理和分类方面具有独特的优势,本文将粗糙集支持向量机应用于遥感影像分类,简要介绍了粗糙集理论的基本概念和支持向量机的基本原理,将粗糙集理论的属性约简作为前置系统,剔除冗余属性,把SVM分类器作为后置系统,对遥感影像进行训练和分类,实验结果表明该模型不仅提高了系统运行的速度,而且分类性能有了一定的提高,为遥感影像分类提供了一条有效途径。
In recent years,with the rapid development of remote sensing technology,processing and classification of remote sensing image has become a hotspot in application studies of remote sensing.Rough set theory and SVM have unique advantages in information processing and classification.this paper applys RS-SVM to remote sensing image classification, briefly introduce the concepts of RS and principle of SVM,attributes reduction in RS theory as preposing system,get rid of redundancy attributes,SVM classifier as postposing system,train and classify remote sensing image.experimental results indicate this model not only raise the operating speed,but also improve classification performance,provide a new effective way in remote sensing image classification.
出处
《电子设计工程》
2012年第23期44-46,共3页
Electronic Design Engineering
基金
国家自然科学基金资助(61102165)
关键词
遥感影像
粗糙集
支持向量机
分类
remote sensing image
rough set
SVM
classification
