Full-Waveform Terrestrial Lidar Data Classification Using Raw Digitized Waveform Signals

Full-waveform (FW) data collected by FW light detection and ranging (lidar) system have long been used to enhance the characterization of the surveyed area. It has already been shown that attributes describing the shape of the echo sig-nal in the feature vector of lidar points improves the performance of land cover classification. In this experiment, rather than fitting the echo waveform with an appropriate parametric model, typically a Gaussian, for feature extraction, the feature vector of each illuminated target is populated with the sam-ples of corresponding digitized waveform. Waveform data is collected over a built environment including both natural and built objects using a full-waveform (FW) terrestrial laser scanning (TLS) system. Results show that samples of raw digitized waveform carry useful information about the physi-cal properties of illuminated targets which lead to a relatively high performance in a multi -class TLS point cloud classifi-cation, where discriminative waveform features are extracted automatically within a proposed convolutional neural network (CNN) model.