Study on image point cloud classification of mountain villages by machine learning
In order to use point cloud technology to better obtain surface information,the built-in optical lens of unmanned aerial vehicle(UAV)AA1300 was used to collect image data and build a 2-D digital orthophoto map(DOM)and GS-1350N lens was hung to collect a 3-D light detection and ranging point cloud.DOM classification was realized by three methods,namely,the k-nearest neighbor(KNN)method,support vector machine(SVM)method,and random forest(RF)method.3-D point cloud was classified by the method with high accuracy in quantitative analysis.The comparative analysis of 2-D and 3-D classification mapping was carried out.The results show that,in 2-D DOM classification,kappa coefficients of RF are 3.74％and 2.16％higher,and the overall accuracy is 4.04％and 2.88％higher than those of KNN and SVM,respectively.The classification results of 2-D can be directly linearly transformed into 3-D point clouds,achieving 2-D and 3-D point cloud classification with a mapping accuracy of 94.15％.Under the same conditions,compared to 2-D/3-D point cloud mapping,direct 3-D point cloud classification can present more complete terrain information.This study indicates that the precise classification of 3-D point clouds can be helpful for better obtaining surface information.
国家科技期刊平台
NSTL
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