A supervoxel-based spectro-spatial approach for 3D urban point cloud labelling

ABSTRACT

Three-dimensional (3D) point cloud labelling of airborne lidar (light detection and ranging) data has promising applications in urban city modelling. Automatic and efficient methods for semantic labelling of airborne urban point cloud data with multiple classes still remains a challenge. We propose a novel 3D object-based classification framework for labelling urban lidar point cloud using a computer vision technique, supervoxels. The supervoxel approach is promising for representing dense lidar point cloud in a compact manner for 3D segmentation and for improving the computational efficiency. Initially, supervoxels are generated by over-segmenting the coloured point cloud using the voxel-based cloud connectivity algorithm in the geometric space. The local connectivity established between supervoxels has been used to produce meaningful and realistic objects (segments). The segments are classified by different machine learning techniques based on several spectral and geometric features extracted from the segments. All the points within a labelled segment are assigned the same segment label. Furthermore, the effect of different feature vectors and varying point density on the classification accuracy has been studied. Results indicate an accurate labelling of points in realistic 3D space conforming to the boundaries of objects. An overall classification accuracy of is achieved by the proposed method. The labelled 3D points can be used directly for the reconstruction of buildings and other man-made objects.

Acknowledgements

The authors would like to thank the Belgian Royal Military Academy for acquiring and providing the data used in this study, and the IEEE GRSS Image Analysis and Data Fusion Technical Committee. The Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) as part of ISPRS WG/III urban classification and 3D building reconstruction. We also thank the two anonymous reviewers for their constructive comments that substantially improved the quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.