Power line classification from airborne LiDAR data via multi-scale neighborhood features

doi:10.13474/j.cnki.11-2246.2019.0106

Abstract

Abstract:

With the rapid development of 3D accurate measurement technology of airborne LiDAR, automatic extraction of power lines from airborne laser point clouds has become an important topic in point cloud data processing and transmission line management. In this paper, we present an automated and versatile framework for power line classification, which consists of four steps:power line candidate point filtering, multi-scale neighborhood type selection, feature extraction based on geospatial structure, and SVM classification. To comprehensively evaluate the proposed algorithm, we calculate each point's feature based on eight levels of scales. Two datasets demonstrate that classification results reach up to 97%, 94%, and 93% in terms of precision rate, recall rate and overall quality. The whole processing time also decreases from 366 s, 256 s to 274 s, 160 s, respectively. Experimental results show that this method can achieve high-precision classification of power lines in complex urban environment.

Key words: airborne LiDAR, urban power line, neighborhood selection, geospatial structure feature, power line classification

CLC Number:

P234.4

WANG Yanjun, LI Kai, LU Lijuan. Power line classification from airborne LiDAR data via multi-scale neighborhood features[J]. 测绘通报, 2019, 0(4): 21-25.

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