3d Classification of Power-line Scene from Airborne Laser Scanning Data Using Random Forests

Since the introduction of Airborne Laser Scanning (ALS) know as an alternative aerial-based data acquisition tool, the requirement of the 3D model reconstruction in both urban and power-line scenes has dramatically increased. Especially, electric utilities including power-line and tower are crucial infrastructures that require considerable resources to be monitored and managed effectively. For the establishment of the power-line scene inventory, its geospatial information such as positions and attributes of power-line networks should be accurately recorded. This paper presents a 3D classification method to classify power-line scene where a few structures including trees, transmission lines and pylons would be vertically overlapped. The research proposes two different scales of feature extractions from a volumetric space and its embedded points for taking advantages of full 3D analysis against conventional 2D pixel-based analysis. With targeted object instances including ground, vegetation, power-line, pylon and building, 21 features to characterize each class are extracted from different segment scale. The Random Forest is investigated as an ensemble decision classifier to classify power-line scenes with extracted features. An ultimate goal of the research is to apply a knowledge-based classifier trained with small training sample to large-scale unlabelled power-line corridors. In order to achieve this goal, this paper conducts a sensitivity analysis in terms of feature extraction scale, feature importance and class distribution over test datasets with or without the separation from training data. Experiments suggest that an optimized classification performance of 96% success rate by Random Forest can be achieved with point-based feature extraction and data sets with relatively equal distribution of the training data.