Decomposing LiDAR waveforms with nonparametric classification methods

摘要

Waveform decomposition is an important step in full-waveform LiDAR remote sensing. Under the Gaussian Mixture Model, the conventional parametric classification algorithm of Expectation-Maximization (EM) is among the most widely applied ones to decompose the waveforms. This paper introduces nonparametric classification methods, such as K-means and mean-shift to decompose the LiDAR waveforms. The experiments demonstrate that a properly selected nonparametric method can model the asymmetry of a waveform, which is ignored in the conventional parametric model based method. Furthermore, the skewness of the decomposed waveform is conspicuous to be utilized for separating bare ground and forest.