A Hierarchical unsupervised method for power line classification from airborne LiDAR data

The automatic classification of power lines from airborne light detection and ranging (LiDAR) data is a crucial task for power supply management. The methods for power line classification can be either supervised or unsupervised. Supervised methods might achieve high accuracy for small areas, but it is time consuming to collect training data over areas of different conditions and complexity. Therefore, unsupervised methods that can automatically work over different areas without sophisticated parameter tuning are in great demand. In this paper, we presented a hierarchical unsupervised LiDAR-based power line classification method that first screened the power line candidate points (including the power line corridor direction detection based on a layered Hough transform, connectivity analysis, and Douglas–Peucker simplification algorithm), followed by the extraction of contextual linear and angular features for each candidate laser points, and finally by setting the feature threshold values to identify the power line points. We tested the method over both forest and urban areas and found that the precision, recall and quality rates were up to 96.7%, 88.8% and 78.3%, respectively, for the test datasets and were higher than the ones from a previously developed supervised classification method. Overall, our approach has the advantages of achieving relatively high accuracy and being relatively fast.