基于K型支持向量机的遥感图像分类新算法  

New algorithm of remote sensing image classification based on K-type support vector machine

作　　者：王静[1] 何建农[1] 

机构地区：[1]福州大学数学与计算机科学学院,福州350108

出　　处：《计算机应用》2012年第10期2832-2835,2839,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(50877010);福建省杰出青年科学基金资助项目(2009J06024)

摘　　要：为了提高遥感图像的分类精度和识别速度,提出了一种基于K型支持向量机(SVM)的遥感图像分类新算法,该算法将灰度共生矩阵提取的纹理特征与光谱特征相结合进行分类。对两组Landsat ETM+数据进行分类仿真实验,结果表明,在多光谱遥感图像的分类中,新算法提高了分类效率、分类精度和泛化能力,K型SVM是一种优于径向基函数SVM的分类器。In order to improve the accuracy and recognition speed of the remote sensing image classification, this paper put forward a new algorithm of remote sensing image classification based on K-type Support Vector Machine （SVM）, and this algorithm used texture features extracted by gray level co-occurrence matrix combined with the spectral ones for classification. The classification simulation tests were done with two groups of Landsat ETM ＋ data. The results show that the new algorithm can improve the accuracy and efficiency of the classification, raise generalization ability, and K-type SVM is a superior classifier to the Radial Basis Function （RBF） SVM.

关 键 词：K型核函数 支持向量机 纹理特征 灰度共生矩阵 遥感图像分类 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]