Cruise 2D decision tree algorithm is one of supervised classification method in data mining and knowledge discovery. It is combined methods of FACT, CART, QUEST decision tree. Using one factor or two factor effects and a bootstrap adjustment prior to variable selection bias, CRUISE can improve the interpretability of its tree and make a easy, efficient and accurate model. The Landsat TM image was classified in Southern Tibet. And the CRUISE 2D decision tree precisely obtained new discriminant classification rules from integrated satellite image , NDVI , NDWI , SBI , GVI and other investigation information based on the same training and testing samples . Finally, the image was classified with the CRUISE 2D decision tree, and the result was compared with that of QUEST (Quick, Unbiased, and Efficient Statistical Tree) and SVM(Support Vector Machine) image classification . The overall accuracy was 94.09% , which was higher 10.86%,10.24% than the overall accuracy of QUEST,SVM . Meanwhile , the Kappa efficient was 0.931 0 , which was higher 0126 8,0.119 6 than the Kappa efficient of QUEST,SVM . The results show that CRUISE 2D effectively improve the unclassified and misclassified pixels in the traditional supervised classification, and it has a very high robustness.Keywords: decision tree ; CRUISE 2D ; classification1