Extraction of exposed carbonatite in karst desertification area using co-location decision tree

A new decision tree induction method, called co-location-based decision tree (CL-DT), is presented in this paper to extract exposed carbonatite in karst rocky desertification area. The proposed algorithm utilizes co-location characteristics of spatial attribute. This paper first presented the co-location mining algorithm, including neighbor graph construction, determination of distinct event-type, pruning non-prevalent co-locations, and inducing co-location rules, and then focused on developing the algorithm of co-location decision tree, which including non-spatial attributes data and spatial data selection, co-location modeling, node merging criteria, and co-location decision tree induction. This paper uses Landsat-5 TM images covering the whole Du´an city in China as the data to verify the proposed method. The experimental results demonstrated that (1) Compared to traditional decision tree, the proposed co-location decision tree has higher accuracy and can make better decision; (2) The training data can be fully played roles in contribution to decision tree induction.