关键词: K型核函数, 支持向量机, 纹理特征, 灰度共生矩阵, 遥感图像分类

Abstract: In order to improve the accuracy and recognition speed of the remote sensing image classification, this paper put forward a new algorithm of remote sensing image classification based on K-type Support Vector Machine （SVM）,and this algorithm used texture features extracted by gray level co-occurrence matrix combined with the spectral ones for classification. The classification simulation tests were done with two groups of Landsat ETM+data. The results show that the new algorithm can improve the accuracy and efficiency of the classification, raise generalization ability, and K-type SVM is a superior classifier to the Radial Basis Function (RBF) SVM.

Key words: K-type kernel function, Support Vector Machine （SVM）, texture feature, Gray Level Co-occurrence Matrix (GLCM), remote sensing image classification