信号与信息处理

灰度和纹理特征组合的SAR影像SVM分类

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  • 武汉大学遥感信息工程学院,武汉430079
付仲良,教授,博导,研究方向:图像处理与分析、地理信息系统,E-mail:fuzhl@263.net

收稿日期: 2011-07-24

  修回日期: 2011-10-10

  网络出版日期: 2012-09-25

SAR Image Classification Based on SVM with Fusion of Gray Scale and Texture Features

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  • School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China  

Received date: 2011-07-24

  Revised date: 2011-10-10

  Online published: 2012-09-25

摘要

针对利用单一特征进行分类的效果不理想、普适性不强等问题,提出了一种灰度和不同纹理特征组合的支持向量机(support vector machine, SVM) 分类方法,将由不同特征组合的SVM分类器用于SAR影像分类,并对几种不同的分类结果进行定性和定量比较分析. 实验结果表明,灰度和不同纹理特征组合的SVM分类方法能够取得较高的分类精度,其结果要优于传统的单一纹理特征分类,是一种有效的SAR影像分类方法.

本文引用格式

付仲良, 张文元, 孟庆祥 . 灰度和纹理特征组合的SAR影像SVM分类[J]. 应用科学学报, 2012 , 30(5) : 498 -504 . DOI: 10.3969/j.issn.0255-8297.2012.05.010

Abstract

This paper proposes a set of SVM classification methods based on fusion of gray scale and texture features. A set of experiments are carried out using the SVM classifiers with feature fusion. Both qualitative and quantitative approaches are applied to assess the classification results. Experimental results demonstrate that the proposed approach is effective for SAR image classification with accuracy higher than those obtained by using single texture feature based algorithms.

参考文献

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