基于Gabor-Krawtchouk矩的支持向量机的分类算法  

Support vector machine algorithm based on Gabor-Krawtchouk moment

作　　者：万水龙 罗国成[1,2] 余彪[1,2] 吉玚[1] 

机构地区：[1]中国人民解放军91960部队,广东汕头515074 [2]南京航空航天大学电子信息工程学院,江苏南京210016

出　　处：《微型机与应用》2014年第3期44-46,共3页Microcomputer & Its Applications

摘　　要：提出了一种基于Gabor变换、Krawtchouk矩和支持向量机的海面溢油SAR图像分类方法。首先对图像进行Gabor滤波,获得不同方向的特征参数,然后提取图像的Krawtchouk矩不变量作为图像的形状特征,最后利用支持向量机进行分类。通过对实验分类结果的定量分析可知,该方法可以获得精度比最小分类模型方法以及最大似然分布模型方法高的分类结果。An oil spill image classification method based on Gabor transformation, Krawtchouk moment and support vector machine is put forward. First of all, it extracts the characteristic parameters in different directions of the image after the Gabor filter. The Krawtchouk moment invariant is used to describe the shape feature of the image. Finally making classification of the feature vector based on SVM. Through the quantitative analysis of the results, the proposed method can gain high accuracy constrast to the minimum classification model method and the maximum likelihood distribution model method.

关 键 词：图像分类 GABOR 变换 Krawtchouk 矩 支持向量机 

分 类 号：TP391.43[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]