Abstract: As one of main classification methods used in data mining,the decision tree algo-rithm is widely used in remote sensing image classification.However,in current studies ofremote sensing image classification,the building of decision trees was found to be dependenton existing data mining software,with little research work focused on decision tree algo-rithms.Based on the BoostTree algorithm,we propose a new algorithm of decision tree en-sembles for remote sensing image classification-AdaTree which is a combination of C4.5andAdaBoost.M1algorithms.In AdaTree,the structure of C4.5and the final hypothesis of Ad-aBoost.M1were modified.With the AdaTree classifier algorithm,apiece of software wasdeveloped for cell-based and object-oriented remote sensing image classification.An experi-ment with Landsat7ETM+ and Wordview2images showed accuracy and efficient improve-ments of the AdaTree classifier when compared with BoostTree and SVM,either in cells-based or object-oriented classification.Its average Kappa coefficients reached 0.905 2and0.939 8.