A Hierarchical Clustering Method to Repair Gaps in Point Clouds of Powerline Corridor for Powerline Extraction

Abstract

Powerline detection is becoming a significant issue for powerline monitoring and maintenance, which further ensures transmission security. As an efficient method, laser scanning has attracted considerable attention in powerline detection for its high precision and robustness during the night period. However, due to occlusion and varying point density, gaps will appear in scans and greatly influence powerline detection by over–clustering, insufficient extraction, or misclassification in existing methods. Moreover, this situation will be worse in terrestrial laser scanning (TLS), because TLS suffers more from gaps due to its unique ground–based scanning mode compared to other laser scanning systems. Thereby, this paper explores a robust method to repair gaps for extracting powerlines from TLS data. Firstly, a hierarchical clustering method is used to extract the powerlines. During the clustering, gaps are repaired based on neighborhood relations of powerline candidates, and repaired gaps can create continuous neighborhood relations that ensure the execution of the clustering method in return. Test results show that the hierarchical clustering method is robust in powerline extraction with repaired gaps. Secondly, reconstruction is performed for further detection. Pylon–powerline connections are found by the slope change method, and powerlines with multi–span are successfully fitted using these connections. Experiment shows that it is feasible to find connections for multi–span reconstruction.