A Classification Method of Point Clouds of Transmission Line Corridor Based on Improved Random Forest and Multi-Scale Features

Abstract

Classification of airborne laser scanning (ALS) point clouds of power lines is of great importance to their reconstruction. However, it is still a difficult task to efficiently and accurately classify the ground, vegetation, power lines and power pylons from ALS point clouds. Therefore, in this paper, a method is proposed to improve the accuracy and efficiency of the classification of point clouds of transmission lines, which is based on improved Random Forest and multi-scale features. The point clouds are filtered by the optimized progressive TIN densification filtering algorithm, then the elevations of the filtered point cloud are normalized. The features of the point cloud at different scales are calculated according to the basic features of the point cloud and the characteristics of transmission lines. The Relief F and Sequential Backward Selection algorithm are used to select the best subset of features to estimate the parameters of the learning model, then an Improved Random Forest classification model is built to classify the point clouds. The proposed method is verified by using three different samples from the study area and the results show that, compared with the methods based on Support Vector Machines, AdaBoost or Random Forest, our method can reduce feature redundancy and has higher classification accuracy and efficiency.

Research on inverse modeling method of transmission line GIM model based on laser point cloud and image feature extraction

“…Then, the details of the model are optimized, the geometric shape is smoothed, and the position relationship is calibrated. Ensure that the model is in line with the actual transmission line condition, and can truly reflect the on-site condition [3][4] . The preliminary GIM model of the transmission line is shown in Figure 2 (a) Preliminary GIM model of transmission line (b) Preliminary GIM model of tower…”

Seperating Power Line Using Lidar Point Elevation and Intensity

“…This is a model that helps to represent a surface from points with discrete spatial distribution. We can represent the surface in terms of adjacent triangles that do not overlap [7]. In each triangle, the surface is represented by a plane.…”

“…In the article [7], the authors use RF algorithm (Random Forest) and multi-scale cluster to perform power line classification. The authors have shown that it is necessary to distribute the features of points in the point cloud to perform the classification problem in different rules.…”

Quantifying Multi-Scale Performance of Geometric Features for Efficient Extraction of Insulators from Point Clouds

“…Moreover, automatic extraction algorithms are susceptible to complex topology structures and system noise. Studies showed that the use of multi-scale features can eliminate or mitigate the impact of noise and high similarity between different objects on extraction accuracy and robustness [9][10][11][12][13]. However, there are few reports on how to use multi-scale geometric features and perform fusion and quantitative evaluation of these features to solve the problems in insulator extraction.…”