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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
Open AccessArticle

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

by 1,2, 1,3,4,*, 1, 1 and 1
1
School of Resource and Environment Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Beijing Institute of Architectural Design (Group) Co., Ltd, 62 Nanlishi Road, Xicheng District, Beijing 100045, China
3
Collaborative Innovation Centre of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
4
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
Remote Sens. 2017, 9(4), 331; https://doi.org/10.3390/rs9040331
Received: 13 February 2017 / Revised: 23 March 2017 / Accepted: 27 March 2017 / Published: 30 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
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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.

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