一种基于 KFCM的 SVM遥感图像机场目标分类算法
作者:
作者单位:

(1.92785部队,河北秦皇岛 066200;2. 海军航空工程学院电子信息工程系,山东烟台 264001;3.海军航空工程学院科研部,山东烟台 264001)

作者简介:

通讯作者:

中图分类号:

TP301.6

基金项目:


An Airport Target Classification Algorithm in Remote Sensing Image Based on KFCM and SVM
Author:
Affiliation:

(1. The 92785th Unit of PLA, Qinhuangdao Hebei 066200, China; 2. Naval Aeronautical and Astronautical University Department of Electronic and Information Engineering, 3.Naval Aeronautical and Astronautical University Department of Scientific Research, Yantai Shandong 264001, China)

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:

    在遥感图像机场目标分类方面,支持向量机(SVM)有着广泛的应用,但由于样本不平衡问题以及不确定性数据的存在,传统 SVM算法的分类精度与效果还无法令人满意。为提高传统 SVM分类器的性能,文章将建立在模糊理论基础上的模糊核 C-均值聚类算法(KFCM)用于处理遥感数据的不确定性问题,并通过聚类分析后的目标子图,剔除非目标样本的同时保留了目标样本,较好地解决了样本不平衡问题。将基于 KFCM的 SVM分类算法用于遥感图像机场目标的分类,实验结果和性能分析表明该算法分类性能优于传统 SVM算法。

    Abstract:

    Support vector machine(SVM) has wide application in remote sensing image airport target classifica. tion, but the traditional SVM algorithm can not get satisfying result in respect of classification accuracy and ef. fect due to sample imbalance and uncertainty data in RS image. To improve the performance of traditional SVM algorithm, the Fuzzy kernel C-means clustering algorithm(KFCM) established in fuzzy theory was used to solve uncertainty problem in RS image, and target subgraph obtained by clustering analysis was used to get rid of nontarget sample and retain target sample, then researched a SVM classification algorithm based on KFCM. Fi. nally a airport classification experiment was done on remote sensing image, experiment results and performance analysis showed that the proposed algorithm had more superior performance than the traditional SVM algorithm.

    参考文献
    相似文献
    引证文献
引用本文

刘峰,张立民,张瑞峰.一种基于 KFCM的 SVM遥感图像机场目标分类算法[J].海军航空大学学报,2013,28(2):161-166
LIU Feng, ZHANG Li-min, ZHANG Rui-feng. An Airport Target Classification Algorithm in Remote Sensing Image Based on KFCM and SVM[J]. JOURNAL OF NAVAL AVIATION UNIVERSITY,2013,28(2):161-166

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: 2016-03-21
  • 出版日期: