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Open AccessArticle

A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation, Wuhan 430079, China
3
Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1636; https://doi.org/10.3390/rs11141636
Received: 15 May 2019 / Revised: 30 June 2019 / Accepted: 3 July 2019 / Published: 10 July 2019
(This article belongs to the Special Issue Remote Sensing based Building Extraction)
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Abstract

Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work. View Full-Text
Keywords: LiDAR point cloud; building extraction; elevation map; Gabor filter; feature fusion LiDAR point cloud; building extraction; elevation map; Gabor filter; feature fusion
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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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Lai, X.; Yang, J.; Li, Y.; Wang, M. A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features. Remote Sens. 2019, 11, 1636.

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