An Improved Random Forest Model Applied to Point Cloud Classification

• Published in: IOP conference series. Materials Science and Engineering 2020-03, Vol.768 (7), p.72037
• Main Authors: Xue, Doudou, Cheng, Yinglei, Shi, Xiaosong, Fei, Yan, Wen, Pei
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Correlation analysis; Correlation coefficients; Decision analysis; Decision trees; Lidar; Three dimensional models
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/768/7/072037

Urban laser radar point cloud building extraction is a hot spot in recent years, but the accurate distinction between vegetation, buildings and man-made objects has always been a difficult point. In this paper, a point cloud classification algorithm based on ICSF and weakly correlated random forest are proposed for the problem of low classification accuracy. Firstly, the data is ground-filtered by ICSF algorithm, then the decision tree is constructed, and correlation analysis is performed based on the maximum mutual information coefficient. A decision tree with the smallest correlation coefficient and the highest precision is selected to form a random forest. Finally, the decision results are weighted and completed. Point cloud classification. This paper validates the model through the Vaihingen city dataset and ranks the importance of the features according to the method of reducing the average precision. Compared with the traditional random forest classification algorithm, the classification accuracy is improved by 4.2%, which shortens the model time.