灰度和纹理特征组合的SAR影像SVM分类

doi:10.3969/j.issn.0255-8297.2012.05.010

应用科学学报 ›› 2012, Vol. 30 ›› Issue (5): 498-504.doi: 10.3969/j.issn.0255-8297.2012.05.010

灰度和纹理特征组合的SAR影像SVM分类

付仲良，张文元，孟庆祥

武汉大学遥感信息工程学院，武汉430079

收稿日期:2011-07-24 修回日期:2011-10-10 出版日期:2012-09-24 发布日期:2012-09-25
通信作者: 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 E-mail:fuzhl@263.net
作者简介: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

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

FU Zhong-liang, ZHANG Wen-yuan, MENG Qing-xiang

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

Received:2011-07-24 Revised:2011-10-10 Online:2012-09-24 Published:2012-09-25

摘要/Abstract

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.

关键词: 纹理, SAR影像分类, 支持向量机, 灰度, 灰度共生矩阵, Gabor 滤波

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.

Key words: SAR image classification, support vector machine (SVM), gray scale, texture, gray level cooccurrence matrix, Gabor filter

中图分类号:

P237

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.

FU Zhong-liang, ZHANG Wen-yuan, MENG Qing-xiang. SAR Image Classification Based on SVM with Fusion of Gray Scale and Texture Features[J]. Journal of Applied Sciences, 2012, 30(5): 498-504.

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灰度和纹理特征组合的SAR影像SVM分类

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

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