The Decision Tree Classification and Its Application Research in Land Cover
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Decision tree classification algorithms have significant potential for remote sensing data classification. In this paper, three different types decision tree classification (UDT, MDT and HDT)are presented. First, the paper discussed the algorithms structure and the algorithms theory of decision tree. Second, decision tree algorithms were used to make land cover classification from remotely sensed data, and the results were compared with conventional statistics classification. The results of this research showed that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. In addition, decision tree algorithms are strictly nonparametric and, therefore, without assumptions regarding the distribution of input data the methods are flexible and robust with respect to general classifications among input features and class labels.