基于特征级融合和支持向量机的飞机识别
DOI:
CSTR:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

TP391.41

基金项目:

国家高技术研究发展计划资助项目(2010AA7080302)


Aircraft recognition scheme based on feature fusion and support vector machine
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    提出一种新的基于组合不变量的飞机识别方法。对不同飞机机型图像,提取Hu矩、仿射矩和归一化傅里叶描述子(NFD)3类不变量进行特征级融合。针对组合不变量取值范围较大问题,提出采用4种归一化方法,结合支持向量机(SVM)以提高飞机识别系统的分类性能。仿真实验表明,提取飞机的组合不变量特征,采用传统神经网络或SVM构建分类器,分类性能均优于单一类别不变量的同类分类器,且SVM的分类性能要优于传统神经网络。同时,当组合不变量要与智能型分类器结合时,采用特定的归一化方法才能取得较好的识别率。

    Abstract:

    A new combination invariants method is proposed for aircraft recognition.For all kinds of aircraft types,Hu moments,affine moments and normalized Fourier descriptors are extracted and combined.As the above invariants are too dispersed,four kinds of normalized methods are studied and combined with support vector machine(SVM) to improve aircraft classification performance.Simulation results show that the classification performance is better by the combination invariants which are combined with support vector machine classifier or neuron network than that by any single kind of invariants which are combined with corresponding classifiers,and the classification performance is better by support vector machine classifier than that by traditional neuron network classifier.Moreover,when combination invariants are sent to intelligent classifiers,a special normalization method can improve the classification performance.

    参考文献
    相似文献
    引证文献
引用本文

朱旭锋,马彩文,刘波.基于特征级融合和支持向量机的飞机识别[J].光电子激光,2011,(11):1710~1713

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期:
  • 出版日期:
文章二维码