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Remote Sens. 2017, 9(3), 288; doi:10.3390/rs9030288

Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests

1
School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China
2
Chinese Academy of Surveying and Mapping, No. 28 Lianhuachixi Road, Beijing 100830, China
*
Author to whom correspondence should be addressed.
Academic Editors: Bailang Yu, Lei Wang, Qiusheng Wu, Guoqing Zhou and Prasad S. Thenkabail
Received: 21 November 2016 / Revised: 26 January 2017 / Accepted: 14 March 2017 / Published: 18 March 2017
(This article belongs to the Special Issue Remote Sensing for 3D Urban Morphology)

Abstract

This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF) based feature selection and classification, and (iv) post-processing. First, a step-wise point cloud segmentation method is proposed to extract three kinds of segments, including planar, smooth and rough surfaces. Second, a segment, rather than an individual point, is taken as the basic processing unit to extract features. Third, RF is employed to select features and classify these segments. Finally, semantic rules are employed to optimize the classification result. Three datasets provided by Open Topography are utilized to test the proposed method. Experiments show that our method achieves a superior classification result with an overall classification accuracy larger than 91.17%, and kappa coefficient larger than 83.79%. View Full-Text
Keywords: airborne laser scanning; point cloud segmentation; random forests; feature extraction; feature selection; semantic airborne laser scanning; point cloud segmentation; random forests; feature extraction; feature selection; semantic
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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).

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Ni, H.; Lin, X.; Zhang, J. Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests. Remote Sens. 2017, 9, 288.

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