Decision Tree Multi-class Classifiers Based on SVM and Applications to Remote Sensing Images
Abstract：In this paper, three kinds of decision tree multi-class classifiers based on SVM are pres- ented by means of three clustering methods, which are respectively clustering with minimum distance of class means, maximum distance of class means and maximum margin criteria. The experiments with AVIRIS remote sensing image are made for testing the validity and advantage of our proposed algo- rithms. The experimental results demonstrate that our methods are significantly better than minimum distance classification, linear discriminant classification, decision tree classification, OAR-SVM and OAO-SVM.
Keywords：support vector machinedecision treeclusteringmaximum margin criterionAVIRIS remote sensing image
Publication Date：2012-01-01
Online Publishing Date：2026-08-28(First online date of this platform, not the publication date of the document)
Pages：5( 6-9,13 )