Improved SVM decision-tree and its application in remote sensing classification
一种改进的SVM决策树及在遥感分类中的应用

This paper presented a SVM decision-tree algorithm based on GA and KNN.First,GA was used to create optimal or near-optimal decision-tree,which defined a novel separability measure.Then in the class phase,standard SVM was used to make binary classification for the divisible nodes,and SVM combined with KNN werc used to classify the fallible nodes.Finally,achieved the multi-classification by the SVM decision-tree.Experimental results show that the proposed method can effectively improve the classification precision of remote sensing image in comparison to traditional classification methods.