random forest method for dimension reduction and point cloud classification based on airborne lidar

Abstract

Exploring automatic point cloud classification method is of great importance to 3D modeling,city land classification,DEM mapping and etc.To overcome the problem that extracting geometric feature for point cloud classification involved neighbor structure meets the challenge that the optimal neighbor scale parameter,high data dimension and complex computation,lacking efficient feature importance analysis and feature selection strategy,this paper proposed a point cloud classification and dimension reduction method based on random forest.After analyzing the characteristic of elevation,intensity and echo of laser points,this paper extracted a total of 6 feature types like normalized height feature,height statistic feature,surface metric feature,spatial distribution feature,echo feature,intensity feature,then built a multi-scale feature parameter from them.Finally,a supervised classification was conducted using a random forest algorithm to optimal the feature set and choose the best feature set to classify the point cloud.Results indicate that,the overall accuracy of the proposed method is 94.3% (Kappa coefficient is 0.922).The proposed method got an improvement in the overall accuracy when compared with no feature selection strategy and SVM classification strategy; The feature importance analysis indicates that the normalized height is the most important feature for the classification.