孙华生;李晓轩;

• 1:辽宁工程技术大学测绘与地理科学学院

摘要(Abstract)：

为了实现对高维遥感图像的快速准确分类,提出了一种基于k均值二叉树支持向量机(SVM)的分类方法。该方法通过对选取的训练样本进行k均值聚类,生成支持向量机分类二叉树,作为确定最佳分类顺序的依据,以降低分类过程中的误差累积并提高整体分类精度,而且可缓解由样本数量不均衡导致的分类误差。该方法可在不进行降维处理的情况下,对高维遥感图像进行快速准确分类。测试结果表明,其分类速度和分类精度都优于传统的支持向量机分类结果。

关键词(KeyWords)： 支持向量机SVM;k均值二叉树;图像分类;高维数据

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(41201454);; 辽宁省大学生创新创业训练计划项目(201410147047)

作者(Authors): 孙华生;李晓轩;

DOI: 10.16251/j.cnki.1009-2307.2016.08.004

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