Point-based Classification of Power Line Corridor Scene Using Random Forests

Abstract

The power line network, interconnecting power generation facilities and their end-users, is a critical infrastructure on which most of our socio-economic activities rely. As society becomes increasingly reliant on electricity, the rapid and effective monitoring of power line safety is critical. In particular, accurately knowing the current geometric and thermal status of power lines and identifying possible encroachments is the most important task in the power line risk management process. To facilitate this task, the correct identification of key objects comprising a power line corridor scene from remotely sensed data is the first important step. In recent years, airborne lidar has been successfully adopted as a cost-effective and accurate data source for mapping the power line corridors. However, in today’s practice, the classification of power line objects using lidar data still relies on labor-intensive data manipulation, and its automation is urgently required. To address this problem, this paper proposes a point-based supervised classification method, which enables the identification of five utility corridor objects (wires, pylons, vegetation, buildings, and low objects) using airborne lidar data. A total of 21 features were investigated to illustrate the horizontal and vertical properties of power line objects. A non-parametric discriminative classifier, Random Forests model, was trained with refined features to label raw laser point clouds. The proposed classifier showed 91.04 percent sample-weighted and 90.07 percent class-weighted classification accuracy, which indicates it could be highly valuable for large-scale, rapid compilations of corridor maps. A sensitivity analysis of the proposed classifier suggested that when compared, training with class-balanced samples improves classification performance over training with unbalanced samples, particularly with corridor objects such as wires and pylons.