Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

Abstract

Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings and trees. In this paper, we presented a new, semi-automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, and (iv) SVM classification. We introduced the power line corridor direction for candidate point filtering and multi-scale slant cylindrical neighborhood for spatial structural features extraction. In a detailed evaluation involving seven scales and four types for local neighborhood selection, 26 structural features, and two datasets, we demonstrated that the use of multi-scale slant cylindrical neighborhood for individual 3D points significantly improved the power line classification. The experiments indicated that precision, recall and quality rate of power line classification is more than 98%, 98% and 97%, respectively. Additionally, we showed that our approach can reduce the whole processing time while achieving high accuracy.

Detecting Power Lines in UAV Images with Convolutional Features and Structured Constraints

“…It is of great importance to investigate the literature on power line detection. Currently, many researchers focus on the analysis of LiDAR data for power line detection [2][3][4], which gives high-precision 3D point cloud data of surroundings. However, these kinds of methods are costly and inconvenient for practical use.…”

Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data

“…We chose the multi-scale local neighborhood to characterize a 3D structure for each considered point according to the previous studies [9][10][11][12]42]. The multi-scale neighborhood could address the multiple levels of detailed presentation of power lines, and was defined with a series of fixed parameters, which included different radii for spherical, vertical cylindrical neighborhoods, and different k values for the k-nearest neighborhood.…”

“…After we tried different kernel functions and compared their corresponding results, we adopted the radial basis function (RBF) kernel, kernel coefficient with four, and automatic scaling of the predicators using a heuristic procedure implemented in Matlab. The RBF kernel was commonly used and validated in many previous SVM applications [26,28,42]. For the RBF SVM, the most important parameters include gamma (the parameter related to the variance of the Gaussian radial basis function) and C (the parameter quantifying how much we penalize the "slack variables" in the objective function).…”

“…The neighborhood types and scales are the two key elements for the local neighborhood. According to the previous studies [9,12,42,59,60], the commonly used neighborhood types are spherical, vertical cylindrical, k-nearest, optimal k-nearest, and slant cylindrical neighborhoods, while the neighborhood scale could be usually divided into single scale and multi-scale types.…”

“…These classifiers contain SVM, RF, DT, NB, DA, and NN, which are popular in the classification of geographical objects from remote sensing images or laser scanning point cloud [16,26,27,33,42]. However, it is difficult to know whether these classifiers are suitable for power line classification or different types of point cloud data.…”