Full-Waveform Terrestrial Lidar Data Classification Using Raw Samples of Digitized Waveform

Full-waveform analysis (FWA) through modeling and decomposition of the digitized echo waveform, measured by a full-waveform (FW) laser scanning system, is typically employed to derive the waveform attributes, including the number of echoes, amplitude and width of each detected echo in the backscattered waveform signal. It has already been shown that such attributes in the feature vector of each measured point can enhance the performance of semantic point cloud segmentation. In this experiment, however, rather than modeling the waveform, the feature vector of each measured target is populated with the raw samples of the waveform related to the target. Random forest classification is used to classify a 3D scene consisting of both natural and man-made targets. The results show that the raw samples of the digitized waveform are discriminative enough to be used as independent features for a multi-class classification task, where, in this experiment, the overall accuracy of 90% was achieved for classifying the
$3D$
scene.