Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof

Article


Tarsha Kurdi, Fayez, Gharineiat, Zahra, Campbell, Glenn, Awrangjeb, Mohammad and Dey, Emon Kumar. 2022. "Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof." Remote Sensing. 14 (2), pp. 1-23. https://doi.org/10.3390/rs14020430
Article Title

Automatic Filtering of Lidar Building Point Cloud in Case of
Trees Associated to Building Roof

ERA Journal ID201448
Article CategoryArticle
AuthorsTarsha Kurdi, Fayez (Author), Gharineiat, Zahra (Author), Campbell, Glenn (Author), Awrangjeb, Mohammad (Author) and Dey, Emon Kumar (Author)
Journal TitleRemote Sensing
Journal Citation14 (2), pp. 1-23
Article Number430
Number of Pages23
Year2022
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2072-4292
Digital Object Identifier (DOI)https://doi.org/10.3390/rs14020430
Web Address (URL)https://www.mdpi.com/2072-4292/14/2/430
Abstract

This paper suggests a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram. This operation aims to select the roof class points from the building point cloud, and the suggested algorithm considers the general case where high trees are associated with the building roof. The Z coordinate histogram is analyzed in order to divide the building point cloud into three zones: the surrounding terrain and low vegetation, the facades, and the tree crowns and/or the roof points. This operation allows the elimination of the first two classes which represent an obstacle toward distinguishing between the roof and the tree points. The analysis of the normal vectors, in addition to the change of curvature factor of the roof class leads to recognizing the high tree crown points. The suggested approach was tested on five datasets with different point densities and urban typology. Regarding the results’ accuracy quantification, the average values of the correctness, the completeness, and the quality indices are used. Their values are, respectively, equal to 97.9%, 97.6%, and 95.6%. These results confirm the high efficacy of the suggested approach.

KeywordsLiDAR; classification; modelling; filtering; segmentation
ANZSRC Field of Research 2020401304. Photogrammetry and remote sensing
Byline AffiliationsSchool of Surveying and Built Environment
Griffith University
Institution of OriginUniversity of Southern Queensland
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