SHORELINE EXTRACTION FROM THE INTEGRATION OF LIDAR POINT CLOUD DATA AND AERIAL ORTHOPHOTOS USING MEAN SHIFT SEGMENTATION
SHORELINE EXTRACTION FROM THE INTEGRATION OF LIDAR POINT CLOUD DATA AND AERIAL ORTHOPHOTOS USING MEAN SHIFT SEGMENTATION
复制标题
使用均值平移分割从激光雷达点云数据和航空正射影像的集成中提取海岸线
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Rongxing Li
中科院分区：
文献类型：
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作者：
I.;Bo Wu;Rongxing Li
提出了一种从激光雷达点云数据和航空正射影像中提取海岸线的方法。首先，采用均值漂移算法进行激光雷达点分割。在Mean Shift算法中，将LiDAR点的水平位置和高程加上从相应正射影像获得的颜色信息作为点特征。由于水面的高程和颜色分布的均质性，分布在水面和地面上的激光雷达点可以用Mean Shift算法以半监督的方式进行分类。其次，利用改进的凸壳算法确定分类后的LiDAR点的边界。海岸线被定义为属于水的激光雷达点和属于非水的激光雷达点之间的分离边界的结果。利用激光雷达数据和在新罕布夏州朴茨茅斯同时获取的正射影像进行的实验表明，推导出的海岸线的精度比激光雷达的点间距有所提高。
A method for shoreline extraction from integrated LiDAR point cloud data and aerial orthophotos is presented. First, a Mean Shift Algorithm is used for LiDAR point segmentation. The horizontal position and elevation of the LiDAR point plus color information obtained from the corresponding orthophoto are used as the point features in the Mean Shift Algorithm. Due to the homogenous nature of the elevation and color distribution of a water surface, LiDAR points distributed on the water surface and on the ground can be classified using Mean Shift Algorithm in a semisupervised manner. Second, a modified convex hull algorithm is used to determine the boundary of the classified LiDAR points. The shoreline is defined as the result of the separation boundary between the LiDAR points belonging to water and those belonging to non-water. The experiment, which used LiDAR data and orthophotos acquired at the same time in Portsmouth, New Hampshire, shows that the accuracy of the derived shoreline is an improvement over LiDAR point spacing.