Point cloud classification is a critical step in the processing of LiDAR data,and exploring new automatic,efficient,high accuracy classification method is of great importance.This paper proposed a new method for point cloud classification by analyzing the feature of optical image and LiDAR data from the same aircraft.First,a TIN model was made by interpolating the LiDAR data which was projection transformed,then the registration fusion of LiDAR and optical image was achieved according to the correspondence vertexes of the two data,and the RGB attribution information from optical image was combined into LiDAR data later.Second,classification feature set was built by extracting the spectral features from optical image and multi-scale geometric features from LiDAR data.Third,a CFS feature selection method was used to reduce dimension of the classification set.Finally,a supervised classification was conducted using a random forest algorithm to classify the point cloud.Results indicate that,the overall accuracy and Kappa coefficient of the proposed method is 89.5% and 0.844,respectively.And the proposed method get an improvement in the overall accuracy by 1.1,5.4 and 14.9 percentage point when compared with no feature selection strategy,only using LiDAR data and only using optical image,respectively.The proposed method not only efficiently reduce the interpolation error when fusion based on point cloud interpolation,but also solve the problem for choosing the optimal scale to extract geometry feature in a certain analytical scale,and the data are able to be processed more efficiently when feature selection is adopted.