A fully automated design of binary decision tree for land cover classification

A fully automated design method of binary decision tree is proposed for land cover classification of remote sensing data. The proposed method has the following features: (1) all possible binary combinations of identification classes are tested as splitting pattern of classes; (2) additional variables by linear combination of paired bands are applied in data segmentation; (3) automatic interaction detector technique is utilized to divide the data set into binary tree nodes. Completely enumerated Landsat MSS data with land-cover data are used for the test of the method and classification accuracy. The authors´ new method is compared with Bayesian discriminant classifier in terms of accuracy and training sample set sizes