Robust Ground Peak Extraction With Range Error Estimation Using Full-Waveform LiDAR

Abstract

Topographic mapping is one of the main applications of airborne LiDAR. Waveform digitization and processing allow for both improved accuracy and higher ground detection rate compared with discrete return systems. Nevertheless, the quality of the ground peak estimation, based on last return extraction, strongly depends on the algorithm used. Best performing methods are too computationally intensive to be used on large data sets. We used Bayesian inference to develop a new ground extraction method whose most original feature is predictive uncertainty computation. It is also fast and robust to ringing and peak overlaps. Obtaining consistent ranging uncertainties is essential for determining the spatial distribution of error on the final product, point cloud, or digital elevation model. The robustness is achieved by a partial deconvolution followed by a Bayesian Gaussian function regression on optimally truncated data, which helps reduce the impact of overlapping peaks from low vegetation. Results from real data are presented, and the gain with respect to classical Gaussian peak fitting is assessed and illustrated.