A Fuzzy Mean-Shift Approach to Lidar Waveform Decomposition


LI Q., Ural S. , ANDERSON J., SHAN J.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, cilt.54, ss.7112-7121, 2016 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 54 Konu: 12
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1109/tgrs.2016.2596105
  • Dergi Adı: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
  • Sayfa Sayıları: ss.7112-7121

Özet

Waveform decomposition is a common step for exploitation of full-waveform lidar data. Much effort has been focused on designing algorithms based on the assumption that the returned waveforms follow a Gaussian mixture model where each component is a Gaussian. However, many real examples show that the waveform components can be neither Gaussian nor symmetric even when the emitted signal is Gaussian or symmetric. This paper proposes a nonparametric mixture model to represent lidar waveforms without any constraints on the shape of the waveform components. A fuzzy mean-shift algorithm is then developed to decompose the waveforms. This approach has the following properties: 1) It does not assume that the waveforms follow any parametric or functional distributions; 2) the waveform decomposition is treated as a fuzzy data clustering problem and the number of components is determined during the time of decomposition; and 3) neither peak selection nor noise floor filtering prior to the decomposition is needed. Experiments are conducted on a dataset collected over a dense forest area where significant skewed waveforms are demonstrated. As the result of the waveform decomposition, a highly dense point cloud is generated, followed by a subsequent filtering step to create a fine digital elevation model. Compared with the conventional expectation-maximization method, the fuzzy mean-shift approach yielded practically comparable and similar results. However, it is about three times faster and tends to lead to slightly fewer artifacts in the resultant digital elevation model.

Waveform decomposition is a common step for exploitation of full-waveform lidar data. Much effort has been focused on designing algorithms based on the assumption that the returned waveforms follow a Gaussian mixture model where each component is a Gaussian. However, many real examples show that the waveform components can be neither Gaussian nor symmetric even when the emitted signal is Gaussian or symmetric. This paper proposes a nonparametric mixture model to represent lidar waveforms without any constraints on the shape of the waveform components. A fuzzy mean-shift algorithm is then developed to decompose the waveforms. This approach has the following properties: 1) It does not assume that the wave- forms follow any parametric or functional distributions; 2) the waveform decomposition is treated as a fuzzy data clustering problem and the number of components is determined during the time of decomposition; and 3) neither peak selection nor noise floor filtering prior to the decomposition is needed. Experiments are conducted on a dataset collected over a dense forest area where significant skewed waveforms are demonstrated. As the result of the waveform decomposition, a highly dense point cloud is generated, followed by a subsequent filtering step to create a fine digital elevation model. Compared with the conventional expectation–maximization method, the fuzzy mean-shift approach yielded practically comparable and similar results. However, it is about three times faster and tends to lead to slightly fewer artifacts in the resultant digital elevation model.