激光技术2024，Vol.48Issue(2)：288-294,7.DOI:10.7510/jgjs.issn.1001-3806.2024.02.022
机器学习参与山区村落影像点云分类的研究
Study on image point cloud classification of mountain villages by machine learning
摘要
Abstract
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.关键词
激光技术/图像处理/机器学习/随机森林分类法/高原山区乡村Key words
laser technique/image processing/machine learning/random forest classification/highland mountain villages分类
测绘与仪器引用本文复制引用
李霞,杨正维,黄俊伟,杨亚复,高莎..机器学习参与山区村落影像点云分类的研究[J].激光技术,2024,48(2):288-294,7.基金项目
国家重点研发计划资助项目(2021YFC3000205-06) （2021YFC3000205-06）
