Acta Optica Sinica, Volume. 39, Issue 2, 0228001(2019)
Classification of Airborne LiDAR Point Cloud Data Based on Multiscale Adaptive Features
To solve the low-classification accuracy problems of urban point clouds in complex environments, we propose a classification method based on multiscale adaptive features herein. Firstly, the classical geometric statistical features and point histogram features are combined; then, the combined feature set is used for classification basis. Random forest is then used to assess the importance of the features and adaptively select important feature sets. Finally, the point clouds are classified based on these multiscale adaptive features. Experimental results reveal that this method can achieve a high-accuracy classification for point clouds in urban areas. The proposed method can be applied to the classification of point cloud data with different resolutions at arbitrary scale.
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Shujuan Yang, Keshu Zhang, Yongshe Shao. Classification of Airborne LiDAR Point Cloud Data Based on Multiscale Adaptive Features[J]. Acta Optica Sinica, 2019, 39(2): 0228001
Category: Remote Sensing and Sensors
Received: May. 21, 2018
Accepted: Jun. 17, 2018
Published Online: May. 10, 2019
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