基于SVM动态集成的高光谱遥感图像分类

Classification of hyperspectral remote sensing images with dynamic support vector machine ensemble

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。

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 validation set samples were more representative by means of appending the misclassified training samples to the validation set.The experimental results show that in comparison to single optimized SVM and other popular SVM ensemble methods the improved dynamic SVM ensemble exhibits the highest classification accuracy,and it could effectively improve the classification precision of hyperspectral remote sensing images.