This study aims to introduce new methods for classifying key features (power lines, pylons, and buildings) comprising utility corridor scene using airborne LiDAR data and modelling power lines in 3D object space. The proposed approach starts from PL scene segmentation using Markov Random Field (MRF), which emphasizes on the roles of spatial context of linear and planar features as in a graphical model. The MRF classifier identifies power line features from linear features extracted from given corridor scenes. The non-power line objects are then investigated in a planar space to sub-classify them into building and non-building class. Based on the classification results, precise localization of individual pylons is conducted through investigating a prior knowledge of contextual relations between power line and pylon. Once the pylon localization is accomplished, a power line span is identified, within which power lines are modelled with catenary curve models in 3D. Once a local catenary curve model is established, this initial model progressively extends to capture entire power line points by adopting model hypothesis and verification. The model parameters are adjusted using a stochastic non-linear square method for producing 3D power line models. An evaluation of the proposed approach is performed over an urban PL corridor area that includes a complex PL scene.
REF used the Markov Random Field (MRF) classifier to separate objects with linear and planner features.
AUTOMATIC POWERLINE SCENE CLASSIFICATION AND RECONSTRUCTION USING AIRBORNE LIDAR DATA
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Computer Science