An adaptive end-to-end classification approach for mobile laser scanning point clouds based on knowledge in urban scenes

An adaptive end-to-end classification approach for mobile laser scanning point clouds based on knowledge in urban scenes

Zheng, M. (TU Delft OLD Department of GIS Technology; Wuhan University)
Wu, Huayi (Wuhan University)
Li, Y. (Hohai University)

2019

It is fundamental for 3D city maps to efficiently classify objects of point clouds in urban scenes. However, it is still a large challenge to obtain massive training samples for point clouds and to sustain the huge training burden. To overcome it, a knowledge-based approach is proposed. The knowledge-based approach can explore discriminating features of objects based on people's understanding of the surrounding environment, which exactly replaces the role of training samples. To implement the approach, a two-step segmentation procedure is carried out in this paper. In particular, Fourier Fitting is applied for second adaptive segmentation to separate points of multiple objects lying within a single group of the first segmentation. Then height difference and three geometrical eigen-features are extracted. In comparison to common classification methods, which need massive training samples, only basic knowledge of objects in urban scenes is needed to build an end-to-end match between objects and extracted features in the proposed approach. In addition, the proposed approach has high computational efficiency because of no heavy training process. Qualitative and quantificational experimental results show the proposed approach has promising performance for object classification in various urban scenes.

3D city maps
Fourier fitting
Geometrical eigen-features
Knowledge
Point cloud

http://resolver.tudelft.nl/uuid:ebdae29e-d339-4ec9-a1fc-b644ea5f4d85

https://doi.org/10.3390/rs11020186

2072-4292

Remote Sensing, 11 (2)

Institutional Repository

journal article

© 2019 M. Zheng, Huayi Wu, Y. Li