一种高斯混合模型组合分类的机载LiDAR城区道路提取方法

城区道路自动提取一直是遥感领域研究的重点和热点之一.针对遥感影像提取易受建筑物和植被遮挡的影响,点云数据提取道路边界又较模糊的不足,提出了一种高斯混合模型组合分类的道路提取方法.该方法利用融合影像即含有色彩信息的点云数据,首先对滤波后点云中的反射强度属性,运用偏度平衡法粗提取道路点云;再对点云数据中的灰度信息和点密度属...

An Urban Road Extraction Method from Airborne LiDAR Based on Gaussian Mixture Model Combination Classification

The automatic extraction of urban roads has been one of the hotspots in the field of remote sensing.Aiming at the shortcomings that remote sensing image is easily affected by tall buildings and vegetation occlusion and the road boundary extracted from point cloud data is relatively fuzzy,a road extraction method based on Gaussian mixture model combination classification is proposed.This method uses point cloud data that contains color information to fuse the image.Firstly,according to property of reflection intensity of the point cloud after filtering,a skewness balancing method is used to extract the road point cloud.Then,the gray information and properties of point density in point cloud data are classified by the Gaussian mixture model for pattern classification to extract the road seed region,which is expanded and constrained by the intensity image.Finally,the snakes and mathematical morphology are used to extract road centerline efficiently.To verify the effectiveness of the proposed method,two sets of LiDAR point cloud data located in a foreign city are used for experiments.The results show that the proposed method can effectively reduce the impact of shadow occlusion on road extraction,the centerline of the extracted road is relatively smooth,and the extraction quality of the road reaches over 85%.