期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B3
页码：219-224
出版社：Copernicus Publications
摘要：This paper explores a novel approach to the extraction of spatial objects from the laser-scanning data using an unsupervised clustering technique. The technique, namely self-organizing maps (SOM), creates a set of neurons following a training process based on the input point clouds with attributes of xyz coordinates and the return intensity of laser-scanning data. The set of neurons constitutes a two dimensional planar map, with which each neuron has best match points from an input point cloud with similar properties. Because of its high capacity in data clustering, outlier detection and visualization, SOM provides a powerful technique for the extraction of spatial objects from laser-scanning data. The approach is validated by a case study applied to a point cloud captured using a terrestrial laser-scanning device
