A method based on multi-threshold classification and attribute morphology is adopted to preprocess lunar image selectively in different gray layers, which ensure different highlight shadow crescent pair and low gray weak edge elliptical crater possess essentially normalized and steady Haar and PHOG feature. The influence and the function of the preprocessing method on partial wavelet Haar and pyramid histogram of oriented gradients feature is probed, and the effects of AdaBoost and SVM used in lunar crater detection are investigated. The integrated craters detecting strategy combining Haar and PHOG features with AdaBoost and SVM classifiers is also studied. The method is proved to have high accuracy and recognition efficiency. Experimental rusults demonstrate that lunar crater recognition radio is proved by 2%~5% via atlribute morphology and assemble classifier compared to traditional methods.