基于多尺度维度特征和SVM的高陡边坡点云数据分类算法研究

Classification Algorithm for Laser Point Clouds of High-steep Slopes Based on Multi-scale Dimensionality Features and SVM

  • 摘要: 为解决复杂场景下高陡边坡点云数据的植被过滤问题,首先研究了高陡边坡上植被和岩石激光点云的多尺度维度特征;然后利用支持向量机(support vector machine,SVM)构建分类器,针对高陡边坡点云数据提出滤波算法,并编制了三维激光点云滤波软件LIDARVIEW。实验数据表明,复杂场景内不同尺度的植被均得到很好识别,滤波算法分类精度较高;算法不受激光点云的密度、遮挡和复杂地形的影响,且适用于机载雷达点云数据的滤波;植被覆盖率高的岩石分类精度高于93%,植被覆盖率低的岩石分类精度高于97%。算法对山丘区有复杂地貌的高陡边坡地形测量具有重大研究意义。

     

    Abstract: In order to solve the vegetation filtering problem of high-steep slope point cloud data in complex scene, the multi-scale dimensionality feature of vegetation and rock laser point cloud on high-steep slope is studied first. Then the SVM (support vector machine is utilized) to build a classifier. Finally a vegetation filtering algorithm of high-steep slope laser point cloud is proposed and a three-dimensional laser point cloud filtering software LIDARVIEW is written . The data shows that: the vegetation of different scales in complex scene is well identified and the classification accuracy of the filtering algorithm is high. The algorithm is not affected by the density, occlusion of laser point cloud as well as the complex topography and it is also suitable for airborne LiDAR point cloud data filtering. The classification accuracy of rock under high vegetation cover is greater than 93%, while under low vegetation cover is higher than 97%. The algorithm has great significance for hilly high-steep slope terrain measurement with complex topography.

     

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