Article: Research on target classification method for dense matching point cloud based on improved random forest algorithm Journal: International Journal of Information and Communication Technology (IJICT) 2022 Vol.21 No.3 pp.290 - 303 Abstract: Aiming at the problems of low accuracy and low efficiency of traditional point cloud target classification methods, this paper designs a new classification method based on improved random forest algorithm. Bagging is combined with random subspace to form a subset of feature training at random, so that the generalisation ability of random forest algorithm can be increased while the data processing speed can be accelerated to avoid overfitting phenomenon. On the basis of extracting geometric features of coloured point clouds, the optimal feature subset for classification is determined, and then the dense matching point clouds are classified using the improved random forest algorithm. Experimental results show that the classification error rate of this method is less than 1%, the average classification process takes only 83.995 s, and the VIM value is all over 0.1, indicating that this method can effectively improve the classification effect of dense matching point cloud targets. Inderscience Publishers - linking academia, business and industry through research

Title: Research on target classification method for dense matching point cloud based on improved random forest algorithm

Authors: Tiebo Sun; Jinhao Liu; Jiangming Kan; Tingting Sui

Addresses: School of Technology, Beijing Forestry University, Beijing 100083, China; Department of Mechanical and Electrical Engineering, Jiangsu Food and Pharmaceutical Science College, Huai'an 223003, Jiangsu, China ' School of Technology, Beijing Forestry University, Beijing 100083, China ' School of Technology, Beijing Forestry University, Beijing 100083, China ' School of Technology, Beijing Forestry University, Beijing 100083, China

Abstract: Aiming at the problems of low accuracy and low efficiency of traditional point cloud target classification methods, this paper designs a new classification method based on improved random forest algorithm. Bagging is combined with random subspace to form a subset of feature training at random, so that the generalisation ability of random forest algorithm can be increased while the data processing speed can be accelerated to avoid overfitting phenomenon. On the basis of extracting geometric features of coloured point clouds, the optimal feature subset for classification is determined, and then the dense matching point clouds are classified using the improved random forest algorithm. Experimental results show that the classification error rate of this method is less than 1%, the average classification process takes only 83.995 s, and the VIM value is all over 0.1, indicating that this method can effectively improve the classification effect of dense matching point cloud targets.

Keywords: improved random forest algorithm; dense matching point cloud; target classification; optimal feature subset.

DOI: 10.1504/IJICT.2022.125541

International Journal of Information and Communication Technology, 2022 Vol.21 No.3, pp.290 - 303

Received: 22 Sep 2020
Accepted: 27 Nov 2020
Published online: 14 Sep 2022 *

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Title: Research on target classification method for dense matching point cloud based on improved random forest algorithm

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