Classification of Terrestrial Lidar Data Directly From Digitized Echo Waveforms

Information derived from full-waveform (FW) light detection and ranging (lidar) data has already been shown to be relevant for point cloud analysis tasks. Relevant waveform attributes to populate the corresponding point’s feature vector are typically provided through a post-processing FW analysis (FWA) technique based on fitting the echo waveform with a parametric function describing the shape and location of the echo pulse in the waveform. Samples of the digitized echo are the primary source for any waveform analysis using parametric functions. On the other hand, for some FW lidar scanning systems, describing the complex system response model using a simple parametric function seems challenging or impractical. Earlier studies have shown the potential of a waveform’s digital samples as relevant waveform attributes for point cloud classification. The main goal of this study is to extend earlier experiments on direct exploitation of returned waveform signals collected by a FW terrestrial laser scanning (TLS) system to multireturn waveform signals for point cloud classification in a built environment. Furthermore, the classification performance on feature vectors containing calibrated waveform attributes, derived from a waveform processing approach performed in real-time by the FW TLS system, is evaluated on multiple-echo waveforms and compared with the classification performance derived from the proposed FW data classification technique via deep learning. Classification performance derived through the proposed technique demonstrates high information content of raw digitized waveform samples. Results show that feature vectors containing samples of digitized echoes carry more information about the physical properties of the target than those containing calibrated waveform attributes.