A fully automated method for segmentation and classification of local field potential recordings. Preliminary resultsClassification of lidar bare-earth points, buildings, vegetation, and small objects based on region growing and angular classifier

Abstract

In recent years, light detection and ranging (lidar) systems have been intensively used in different urban applications such as map updating, communication analysis, virtual city modelling, risk assessment, and monitoring. A prerequisite to enhance lidar data content is to differentiate ground (bare earth) points that yield digital terrain models and off-terrain points in order to classify urban objects and vegetation. The increasing demand for a fast and efficient algorithm to extract three-dimensional urban features was the motive behind this work. A new combined approach to extract bare-earth points is proposed, and a novel methodology to automatically classify airborne laser data into different objects in an urban area is presented. In addition, a new concept of angular classification is introduced to differentiate buildings from vegetation and other small objects. The new angular classifier analyses the distribution of bare-earth points around unclassified point clusters to determine whether a cluster can be classified either as building or as vegetation. The experimental results confirm the high accuracy achieved to automatically classify urban objects in flat complex areas.