Using Decision Tree And SVM To Study The Method Of RS Image Classification

Since remote sensing technology was brought forward in 1960s, it has provided information to many areas powerfully. In order to using remote sensing technology better, people has had significant progress in all directions of the remote sensing research. Remote sensing image need to be interpreted at first, to get the thematic information, then it can be used in Geography and other works. Automatic interpretation of computer is lower in accuracy than manual interpretation, but it has advantage in efficiency innately. With the development of classification research, the accuracy of automatic interpretation of computer has improved continuously, and it can meet the demand of certain specific works. Because of the magnanimity of the remote sensing data, and the urgent need of getting information rapidly in some areas, the speed of has been paid more and more attention, and that gives the birth to the research in algorithm of improving the speed of classification. In this paper, it starts from the classification efficiency to study the classification algorithm. It designed to meet the requirements of accuracy, finding algorithm that can improve classification efficiency.Traditional methods of pattern recognition are widely used in automatic interpretation of computer, such as the ISODATA of unsupervised classification, the Maximum likelihood of supervised classification. But these methods are affected by the resolution of remote sensing images and the phenomenon of "the same thing different features" and "different things the same feature", so it makes wrong classification with low accuracy. With the development of remote sensing technology, classification of syntactic patterns has occurred in years, such as ANN, Fuzzy Models, Decision tree, Support Vector Machine (SVM). In this paper, typical methods of tows are selected to execute the comparative study, including the ISODATA, Maximum likelihood, Decision tree and Support Vector Machine (SVM). And classification method of combining Decision tree and Support Vector Machine (SVM) together is brought forward ultimately.After research, the classification accuracy of four methods are of great differences. The accuracy of SVM is the highest, Decision tree and Maximum likelihood get the second place, ISODATA is the lowest. However, in the efficiency of classification the Decision tree needs the lest time, Maximum likelihood and ISODATA get the second place, SVM need the most. The method of combining Decision tree and Support Vector Machine (SVM) together in this paper gets the same accuracy as SVM, but the time needing is reduced rapidly, slightly less than ISODATA or Maximum likelihood. It gets the purpose that in premise of accuracy unchanged basically to improve the classification efficiency. It has advantage in both accuracy and efficiency.