A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features
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 technol...
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doaj-1f630a34c5bf4b3997c1b2a8247756f62020-11-25T01:09:09ZengMDPI AGRemote Sensing2072-42922019-07-011114163610.3390/rs11141636rs11141636A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture FeaturesXudong Lai0Jingru Yang1Yongxu Li2Mingwei Wang3School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaKey Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation, Wuhan 430079, ChinaBuilding 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.https://www.mdpi.com/2072-4292/11/14/1636LiDAR point cloudbuilding extractionelevation mapGabor filterfeature fusion |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xudong Lai Jingru Yang Yongxu Li Mingwei Wang |
spellingShingle |
Xudong Lai Jingru Yang Yongxu Li Mingwei Wang A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features Remote Sensing LiDAR point cloud building extraction elevation map Gabor filter feature fusion |
author_facet |
Xudong Lai Jingru Yang Yongxu Li Mingwei Wang |
author_sort |
Xudong Lai |
title |
A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features |
title_short |
A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features |
title_full |
A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features |
title_fullStr |
A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features |
title_full_unstemmed |
A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features |
title_sort |
building extraction approach based on the fusion of lidar point cloud and elevation map texture features |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-07-01 |
description |
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. |
topic |
LiDAR point cloud building extraction elevation map Gabor filter feature fusion |
url |
https://www.mdpi.com/2072-4292/11/14/1636 |
work_keys_str_mv |
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