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：2016
卷号：XL-3/W4
出版社：Copernicus Publications
摘要：The application of three dimensional building models has become more and more important for urban planning, enhanced navigation and visualization of touristy or historic objects. 3D models can be used to describe complex urban scenes. The automatic generation of 3D models using elevation data is a challenge for actual research. Especially extracting planes edges and corners of man made objects is of great interest. This paper deals with the automatic classification of points by utilizing the eigenvalues of the covariance within the close neighbourhood. The method is based on the analysis of 3D point clouds derived from Laser scanner data. For each 3D point additional structural features by considering the neighbourhood are calculated. Invariance with respect to position, scale and rotation is achieved by normalization of the features. For classification the derived features are compared with analytical calculated as well as trained feature values for typical object structures. For the generation of a training data set several point sets with different density and varying noise are generated and exploited. The result of the investigations is that the quality of the classification using the analytical eigenvalues as reference is not harmful in comparison to the trained data set for a small noise. Therefore for all structures presented here it is not necessary to use training data sets instead of an unsupervised classification based on the analytical eigenvalues. Weighting the calculated distances in the eigenvalue space dependent on the structure type improves the classification result. Due to this classification all points which may belong to a building edge are selected. Assembling these points to lines the 3D borders of the objects were achieved. The algorithm is tested for several urban scenes and the results are discussed
关键词：Laser data; point cloud; classification; nearest neighbour; covariance; eigenvalues
