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Remote Sens. 2013, 5(4), 1624-1650; doi:10.3390/rs5041624
Article

Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation

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Received: 12 January 2013 / Revised: 6 March 2013 / Accepted: 13 March 2013 / Published: 28 March 2013
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)

Abstract

Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from LiDAR sensors is presented. The 3D point cloud is first segmented into voxels, which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metric that combines both segmentation and classification results simultaneously is presented. The effects of voxel size and incorporation of RGB color and laser reflectance intensity on the classification results are also discussed. The method is evaluated on standard data sets using different metrics to demonstrate its efficacy.
Keywords: segmentation; 3D point cloud; super-voxel; classification; urban scene; 3D objects segmentation; 3D point cloud; super-voxel; classification; urban scene; 3D objects
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.

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Aijazi, A.K.; Checchin, P.; Trassoudaine, L. Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation. Remote Sens. 2013, 5, 1624-1650.

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