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SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas
Key Laboratory of Mapping from Space, Chinese Academy of Surveying and Mapping, Lianhuachixi Road No. 28, Beijing 100830, China
* Author to whom correspondence should be addressed.
Received: 4 May 2013; in revised form: 17 July 2013 / Accepted: 18 July 2013 / Published: 31 July 2013
Abstract: Object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM) is utilized to classify the segments, and connected component analysis for 3D point clouds is proposed to optimize the original classification results. Three datasets with different point densities and complexities are employed to test our method. Experiments suggest that the proposed method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications.
Keywords: airborne LiDAR; object-based classification; point clouds; segmentation; SVM
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Cite This Article
MDPI and ACS Style
Zhang, J.; Lin, X.; Ning, X. SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas. Remote Sens. 2013, 5, 3749-3775.
Zhang J, Lin X, Ning X. SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas. Remote Sensing. 2013; 5(8):3749-3775.
Zhang, Jixian; Lin, Xiangguo; Ning, Xiaogang. 2013. "SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas." Remote Sens. 5, no. 8: 3749-3775.