Automated remote sensing image classification method based on Kmeans and SVM
基于Kmeans与SVM结合的遥感图像全自动分类方法*

The supervised learning algorithm was usually used for remote sensing image classification, but its training samples need to be chosen by manual, which was boring and sometimes even difficult. However, in unsupervised learning algorithm classification result was often not satisfactory. According to these limitations, an automated remote sensing image classification method of combining K-means algorithm with SVM. In new method, at first K-means algorithm was used to cluster original data points, and then according to the number and sparse degree of points in each class, some points as labeled samples were chosen to train SVM, at last SVM was utilized to reclassify original data points. Experimental results for Iris data and remote sensing data verify the validity of the proposed method.