DECOMPOSING LIDAR WAVEFORMS WITH NONPARAMETRIC CLASSIFICATION METHODS

Li Q., Ural S., SHAN J.

36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, Çin, 10 - 15 Temmuz 2016, ss.5573-5576 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/igarss.2016.7730455
  • Basıldığı Şehir: Beijing
  • Basıldığı Ülke: Çin
  • Sayfa Sayıları: ss.5573-5576
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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.