Decision tree coupled with feature optimization for object-based classification of ZY-1-02C satellite images

The Separability and Thresholds (SEaTH) algorithm calculates the the SEparability and the corresponding THresholds of object classes for any number of given features. However, it is applicable only to the normally distributed training data. To cope with the problem, The Classification And Regression Tree (CART) coupled with SEaTH for object-based classification approach is proposed in the paper. The idea of this method is derived from the merits of the CART which can effectively analyze the non-normally distributed data and automatically create the classification tree. A comparison of classification results demonstrate that the solution for object-based classification proposed in this article can be used to obtain a higher classification accuracy than SEaTH classification.