基于支持向量机的遥感图像分类研究综述

摘要/Abstract

摘要： 遥感技术是目前用于研究地球矿产资源与能源的重要技术手段,遥感图像分类在遥感技术应用中起着关键作用。支持向量机(Support Vector Machines,SVM)是基于VC维(Vapnik-Chervonenkis Dimension)理论和结构风险最小化原理的机器学习方法,已被广泛应用于实际的遥感影像分类中。对国内外学者对此做的大量研究成果进行了系统的总结。对基于支持向量机的遥感图像分类方法进行了层次性梳理,不但纵向分析和比较了每类方法的原理及优缺点,而且对各类方法进行了横向比较和分析,较为系统和完整地概括了基于支持向量机的遥感影像分类方法的研究现状。最后指出了支持向量机算法应用于遥感图像分类的未来发展方向。

关键词: 遥感图像,分类,支持向量机

Abstract: Remote sensing technology is an important technology of studying the earth mineral resources and energy.Remote sensing image classification plays a key role in the application of remote sensing technology.Support vector machine(SVM) is a machine learning method based on VC dimension(Vapnik-Chervonenkis Dimension) theory and structural risk minimization principle,which has been widely used in the actual remote sensing image classification.Domestic and foreign scholars have done a lot of research about it and these studies were systematically summarized in this paper.The remote sensing image classification methods based on the support vector machine is reviewed hierarchically,that is,the principle and characteristics of each method were analyzed and compared laterally and vertically.The research status of the remote sensing image classification based on the support vector machine was summarized systematically and completely in this paper.Finally,the future development direction of support vector machine algorithm applied in the remote sensing image classification was pointed out.

Key words: Remote sensing images,Classification,Support vector machine (SVM)

王振武,孙佳骏,于忠义,卜异亚. 基于支持向量机的遥感图像分类研究综述[J]. 计算机科学, 2016, 43(9): 11-17. https://doi.org/10.11896/j.issn.1002-137X.2016.09.002

WANG Zhen-wu, SUN Jai-jun,  YU Zhong-yi and BU Yi-ya. Review of Remote Sensing Image Classification Based on Support Vector Machine[J]. Computer Science, 2016, 43(9): 11-17. https://doi.org/10.11896/j.issn.1002-137X.2016.09.002

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