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Title: Automating parameter learning for classifying terrestrial LiDAR point cloud using 2D land cover maps
Authors: Feng, C.-C 
Guo, Z 
Keywords: Three dimensional computer graphics
National University of Singapore
Neighborhood scale
Point cloud
Sample selection
Segment-based classification
Terrestrial laser scanning
Three-dimensional (3D) model
Classification (of information)
Issue Date: 2018
Citation: Feng, C.-C, Guo, Z (2018). Automating parameter learning for classifying terrestrial LiDAR point cloud using 2D land cover maps. Remote Sensing 10 (8) : 1192. ScholarBank@NUS Repository.
Abstract: The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. In order to increase the level of automation in the point cloud classification, this study proposes a segment-based parameter learning method that incorporates a two-dimensional (2D) land cover map, in which a strategy of fusing the 2D land cover map and the 3D points is first adopted to create labelled samples, and a formalized procedure is then implemented to automatically learn the following parameters of point cloud classification: the optimal scale of the neighborhood for segmentation, optimal feature set, and the training classifier. It comprises four main steps, namely: (1) point cloud segmentation; (2) sample selection; (3) optimal feature set selection; and (4) point cloud classification. Three datasets containing the point cloud data were used in this study to validate the efficiency of the proposed method. The first two datasets cover two areas of the National University of Singapore (NUS) campus while the third dataset is a widely used benchmark point cloud dataset of Oakland, Pennsylvania. The classification parameters were learned from the first dataset consisting of a terrestrial laser-scanning data and a 2D land cover map, and were subsequently used to classify both of the NUS datasets. The evaluation of the classification results showed overall accuracies of 94.07% and 91.13%, respectively, indicating that the transition of the knowledge learned from one dataset to another was satisfactory. The classification of the Oakland dataset achieved an overall accuracy of 97.08%, which further verified the transferability of the proposed approach. An experiment of the point-based classification was also conducted on the first dataset and the result was compared to that of the segment-based classification. The evaluation revealed that the overall accuracy of the segment-based classification is indeed higher than that of the point-based classification, demonstrating the advantage of the segment-based approaches. © 2018 by the authors.
Source Title: Remote Sensing
ISSN: 20724292
DOI: 10.3390/rs10081192
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