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基于SVM动态集成的高光谱遥感图像分类
引用本文:牛鹏,魏维.基于SVM动态集成的高光谱遥感图像分类[J].计算机应用,2010,30(6).
作者姓名:牛鹏  魏维
作者单位:西安通信学院通信装备管理系,西安,710106
摘    要:在Bagging支持向量机(SVM)的基础上,将动态分类器集选择技术用于SVM的集成学习,研究了SVM动态集成在高光谱遥感图像分类中的应用。结合高光谱数据特性,通过随机选取特征子空间和反馈学习改进了BaggingSVM方法;通过引进加性复合距离改善了K近邻局部空间的计算方法;通过将错分的训练样本添加到验证集增强了验证集样本的代表性。实验结果表明,与单个优化的SVM和其他常见的SVM集成方法相比,改进后的SVM动态集成分类精度最高,能有效地提高高光谱遥感图像的分类精度。

关 键 词:高光谱  分类  动态分类器集选择  集成学习  SVM动态集成  

Classification of hyperspectral remote sensing images with dynamic support vector machine ensemble
NIU Peng,WEI Wei.Classification of hyperspectral remote sensing images with dynamic support vector machine ensemble[J].journal of Computer Applications,2010,30(6).
Authors:NIU Peng  WEI Wei
Institution:Department of Communication Equipments Management/a>;Xi'an Communications Institute/a>;Xi'an Shaanxi 710106/a>;China
Abstract:Based on Bagging Support Vector Machine (SVM),this paper applied dynamic ensemble selection technique to the SVM ensemble learning,and investigated the application of dynamic SVM ensemble to the classification of hyperspectral remote sensing images.Considering the characteristics of hyperspectral data,Bagging SVM was improved by selecting feature subspace randomly and feedback learning;the algorithm of computing local area of K nearest neighbors was ameliorated through adopting plus composite distance;the v...
Keywords:hyperspectral  classification  dynamic ensemble selection  ensemble learning  dynamic SVM ensemble  
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