Classification of mobile laser scanning point clouds from height features

Classification of mobile laser scanning point clouds from height features

Zheng, M. (TU Delft OLD Department of GIS Technology; Wuhan University)
Lemmens, M.J.P.M. (TU Delft OLD Department of GIS Technology)
van Oosterom, P.J.M. (TU Delft OLD Department of GIS Technology)

2017

The demand for 3D maps of cities and road networks is steadily growing and mobile laser scanning (MLS) systems are often the preferred geo-data acquisition method for capturing such scenes. Because MLS systems are mounted on cars or vans they can acquire billions of points of road scenes within a few hours of survey. Manual processing of point clouds is labour intensive and thus time consuming and expensive. Hence, the need for rapid and automated methods for 3D mapping of dense point clouds is growing exponentially. The last five years the research on automated 3D mapping of MLS data has tremendously intensified. In this paper, we present our work on automated classification of MLS point clouds. In the present stage of the research we exploited three features - two height components and one reflectance value, and achieved an overall accuracy of 73%, which is really encouraging for further refining our approach.

Classification
Feature extraction
Mobile laser scanning
Point clouds
Urban area
Vertical objects

http://resolver.tudelft.nl/uuid:f80c4b35-2b8d-4b36-ba5f-ba1ebce071af

https://doi.org/10.5194/isprs-archives-XLII-2-W7-321-2017

ISPRS

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W7

ISPRS Geospatial Week 2017, 2017-09-18 → 2017-09-22, Wuhan, China

Institutional Repository

conference paper

© 2017 M. Zheng, M.J.P.M. Lemmens, P.J.M. van Oosterom