A Segmentation Procedure of Lidar Data by Applying Mixed Parametric and Nonparametric Models
نویسندگان
چکیده
The paper proposes a segmentation procedure inspired to a robust LIDaR filtering data method recently introduced by the authors. The method is based on the application of a Simultaneous AutoRegressive (SAR) model for describing a trend surface and of an iterative Forward Search (FS) algorithm to detect clusters of non-stationary data. The procedure consists in an automatic process to identify raw clusters of data relating to the geometrical configurations to be segmented with the robust iterative SAR-FS parametric model. The search of homogenous clusters of points is carried out by applying a local polynomial regression algorithm, automatically adapted to the morphological variability of the LIDaR points. The combination of the parametric and nonparametric models in a mixed analytical procedure makes it possible to optimize the efficiency of the segmentation and dramatically reduce the requirements of computational memory and time consuming. Some significant experiments make it possible to evidence the potential of the method proposed.
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