DECOMPOSING LIDAR WAVEFORMS WITH NONPARAMETRIC CLASSIFICATION METHODS

Li Q., Ural S., SHAN J.

36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10 - 15 July 2016, pp.5573-5576 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/igarss.2016.7730455
  • City: Beijing
  • Country: China
  • Page Numbers: pp.5573-5576
  • Hacettepe University Affiliated: Yes

Abstract

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.