Next Article in Journal
Niche Modeling of Dengue Fever Using Remotely Sensed Environmental Factors and Boosted Regression Trees
Previous Article in Journal
Correction: Chen, N. et al. NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on OzNet in Southeastern Australia. Remote Sens. 2017, 9, 51
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(4), 331; doi:10.3390/rs9040331

A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation

1,3,4,* , 1
School of Resource and Environment Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Beijing Institute of Architectural Design (Group) Co., Ltd, 62 Nanlishi Road, Xicheng District, Beijing 100045, China
Collaborative Innovation Centre of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
The Key Laboratory of GIS, Ministry of Education, China, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Author to whom correspondence should be addressed.
Academic Editors: Xiaofeng Yang and Prasad S. Thenkabai
Received: 13 February 2017 / Revised: 23 March 2017 / Accepted: 27 March 2017 / Published: 30 March 2017
View Full-Text   |   Download PDF [8480 KB, uploaded 31 March 2017]   |  


The segmentation of urban scene mobile laser scanning (MLS) data into meaningful street objects is a great challenge due to the scene complexity of street environments, especially in the vicinity of street objects such as poles and trees. This paper proposes a three-stage method for the segmentation of urban MLS data at the object level. The original unorganized point cloud is first voxelized, and all information needed is stored in the voxels. These voxels are then classified as ground and non-ground voxels. In the second stage, the whole scene is segmented into clusters by applying a density-based clustering method based on two key parameters: local density and minimum distance. In the third stage, a merging step and a re-assignment processing step are applied to address the over-segmentation problem and noise points, respectively. We tested the effectiveness of the proposed methods on two urban MLS datasets. The overall accuracies of the segmentation results for the two test sites are 98.3% and 97%, thereby validating the effectiveness of the proposed method. View Full-Text
Keywords: mobile laser scanning; voxel; clustering; segmentation mobile laser scanning; voxel; clustering; segmentation

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Li, Y.; Li, L.; Li, D.; Yang, F.; Liu, Y. A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation. Remote Sens. 2017, 9, 331.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top