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

Remote Sensing

Mohammad Awrangjeb¹  Emon Kumar Dey¹  Fayez Tarsha Kurdi²  Glenn Campbell²  Zahra Gharineiat² 

[1] Institute of Integrated and Intelligent Systems, Griffith University, Nathan, QLD 4111, Australia;School of Surveying and Built Environment, Faculty of Health, Engineering and Sciences, University of Southern Queensland, Springfield Campus, Springfield, QLD 4300, Australia;

关键词: LiDAR;    classification;    modelling;    filtering;    segmentation;   

DOI  :  10.3390/rs14020430

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown