为解决复杂场景下城区点云分类精度不高的问题,提出了基于多尺度自适应特征的分类方法。首先,对经典几何统计特征和点直方图特征进行组合,将组合特征集作为分类依据;然后采用随机森林法评估特征的重要性,并自适应选取重要的特征集;最后基于多尺度自适应特征实现了点云的分类。实验结果表明:该方法能实现城区点云的高精度分类,能适合任意尺度下不同分辨率点云数据的分类。

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.