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Airborne LIDAR can flexibly obtain point cloud data with three-dimensional structural information, which can improve its effectiveness of automatic target recognition in the complex environment. Compared with 2D information, 3D information performs better in separating objects and background. However, an aircraft platform can have a negative influence on LIDAR obtained data because of various flight attitudes, flight heights and atmospheric disturbances. A structure of global feature based 3D automatic target recognition method for airborne LIDAR is proposed, which is composed of offline phase and online phase. The performance of four global feature descriptors is compared. Considering the summed volume region (SVR) discrepancy in real objects, SVR selection is added into the pre-processing operations to eliminate mismatching clusters compared with the interested target. Highly reliable simulated data are obtained under various sensor’s altitudes, detection distances and atmospheric disturbances. The final experiments results show that the added step increases the recognition rate by above 2.4% and decreases the execution time by about 33%.