A Comparative Study on Airborne Lidar Waveform Decomposition Methods

Abstract

This paper applies pattern recognition methods to airborne lidar waveform decomposition. The parametric and nonparametric approaches are compared in the experiments. The popular Gaussian mixture model (GMM) and expectation-maximization (EM) decomposition algorithm are selected as the parametric approach. Nonparametric mixture model (NMM) and fuzzy mean-shift (FMS) are used as the nonparametric approach. We first run our experiment on simulated waveforms. The experiment setup is in favor of the parametric approach because GMM is used to generate the waveforms. We show that both parametric and nonparametric approaches return satisfying results on the simulated mixture of Gaussian components. In the second experiment, real data acquired with an airborne lidar are used. We find that NMM fits the data better than GMM because the Gaussian assumption is not well satisfied in the real dataset. Considering that the emitted signals of a laser scanner may even not satisfy the Gaussian assumption, we conclude that nonparametric approaches should generally be utilized for practical applications.