Abstract
Point cloud classification is an essential requirement for effectively utilizing point cloud data acquired by Terrestrial laser scanning (TLS). Neighborhood selection, feature selection and extraction, and classification of points based on the respective features constitute the commonly used workflow of point cloud classification. Feature selection and extraction has been the focus of many studies, and the choice of different features has had a great impact on classification results. In previous studies, geometric features were widely used for TLS point cloud classification, and only a few studies investigated the potential of both intensity and color on classification using TLS point cloud. In this paper, the geometric features, color features, and intensity features were extracted based on a supervoxel neighborhood. In addition, the original intensity was also corrected for range effect, which is why the corrected intensity features were also extracted. The different combinations of these features were tested on four real-world data sets. Experimental results demonstrate that both color and intensity features can complement the geometric features to help improve the classification results. Furthermore, the combination of geometric features, color features, and corrected intensity features together achieves the highest accuracy in our test.
Highlights
Terrestrial laser scanning (TLS) devices have been widely used to quickly acquire 3D spatial information of large-scale urban scenes
We carefully analyzed the performance of six different feature sets in TLS point cloud classification
A supervoxel neighborhood was used as a support region for feature extraction for the first time
Summary
Terrestrial laser scanning (TLS) devices have been widely used to quickly acquire 3D spatial information of large-scale urban scenes. The common workflow of 3D point cloud classification involves neighborhood selection, feature selection and extraction, and classification of points based on the respective features [1]. The spherical neighborhood and cylindrical neighborhood are often used These neighborhoods with fix-bound radiuses may be inappropriate for TLS point clouds because of their varying point densities. The K-nearest neighbors can provide irregular neighborhood sizes depending on the density of its point cloud and its flexibility. To improve the computational efficiency, some authors have proposed voxel- or supervoxel-based neighborhoods for feature extraction. Plaza-Leiva et al utilizes voxel-based neighborhoods to extract features. In their method, the points in each voxel
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