Comparison of clustering methods based on Kohonen neural network in remote sensing classification
基于Kohonen神经网络聚类方法在遥感分类中的比较

Three kinds of clustering methods,including KCN(Kohonen Clustering Network), FKCN(Fuzzy c-Means based Kohonen Clustering Network) and EPKCN(Evolutionary Programming based Kohonen Clustering Network) that were applied in the classification of remote sensing image,were discussed.Experiments show that these unsupervised learning methods had different characters in classifying land use/cover of remote sensing.To EPKCN,the vision effect of classification is best and the rate of single iteration is fastest;To FKCN,when the training process trends to convergence,the total training rate is fastest.However,taking into count the demand of land use/cover classification in remote sensing,EPKCN is the best one in these three algorithms,and can be applied in unsupervised classification of remote sensing land use/cover.