测地空间中泊松碟采样的地形模型约简方法

Poisson Disk Sampling in Geodesic Metric for DEM Simplification

针对地形模型的数据冗余问题,提出在测地空间中进行均匀采样的策略,实现数据精简的目的.该方法随机地从原始地面模型中提取点,根据当前点与已有采样点的测地距离确定是否将其加入到采样点集,重复该过程直至无新的采样点加入.通过定义加权测地距离,该方法能有效调整不同区域的采样概率,从而使精简后的模型能有效保持原始数据的特征信息.该算法原理简单,实现容易,约简后数据的分布具有良好的可视化效果.

A geometric approach for DEMs simplification is proposed. The main idea is to uniformly pick points from DEM in the sense of geodesic metric,resulting in terrain-adaptive samples in Euclidean metric. This method randomly sample point from mesh nodes,then judge if the point can be accepted or not according to its geodesic distance from sampled point set,and repeat the whole process until no points can be added. By defining weighted geodesic distance positively related to terrain variation, results with more points in ragged terrain areas and sparse points in flat areas can be obtained. Moreover, the distribution of samples is very fit for high performance terrain visualization. This method is really simple and can retain the topographical details more effectively.