Featured research paper (waveform processing)

Robust Ground Peak Extraction With Range Error Estimation Using Full-Waveform LiDAR
A. Jalobeanu, G. Gonçalves
IEEE Geoscience and Remote Sensing Letters
Volume 11, Issue 7 – DOI 10.1109/LGRS.2013.2288152

Topographic mapping is one of the main applications of airborne LiDAR. Waveform digitization and processing allow for both an improved accuracy and a higher ground detection rate compared to 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 datasets. 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 DEM. 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.

Waveform deconvolution

Left: received high amplitude waveforms (Riegl LMS-Q680i low channel), centered and normalized; original (red) and after partial deconvolu- tion (blue). Right: discrete convolution kernel H, calibrated from the data.

Robust ground peak estimation examples

Examples of ground peak extraction from real waveforms (Riegl LMS-Q680i). Full and
truncated estimators (thin lines), and automatically selected estimator (thick line)