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

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.

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.