利用质心Voronoi图对地形自适应简化的算法

An Adaptive Terrain Simplification Algorithm Based on Centroidal Voronoi Diagram

  • 摘要: 地形简化算法利用少量有效的地形信息表达整体地形,能很好地解决海量地形数据与计算机硬件之间的矛盾,同时满足多尺度地形应用需求。针对现有地形简化算法难以兼顾局部地形起伏与地形整体特征的问题,提出一种基于质心Voronoi图的地形自适应简化算法。首先,利用质心Voronoi图的特点,以地形起伏度作为密度函数生成质心Voronoi图;然后,利用分布在地形起伏较大区域的质心Voronoi图种子点及大多分布在地形特征线上的Voronoi区域顶点重构地形;最后,通过原始地形与重构地形的特征线验证地形简化的效果,并与三维道格拉斯-普克(3D Douglas-Peucker,3D DP)算法进行精度对比。实验结果表明,从简化地形中提取的山脊线、山谷线、等高线等地形特征线与原始地形的重叠度均较高,算法能较好地保持地形整体特征;且在相同的简化级别下,算法的简化误差小于3D DP算法,具有较高的地形简化精度。

     

    Abstract:
      Objectives  Terrain simplification algorithms, which use minimal amount of effective terrain information to express the overall terrain, can solve the contradiction between massive terrain data and computer hardware, and meet the needs of multi-scale terrain applications. However, it is difficult for most of the existing terrain simplification algorithms to take local fluctuations and overall characteristics of the terrain into account at the same time. Aiming at these deficiencies, an adaptive terrain simplification algorithm based on centroidal Voronoi diagram is proposed.
      Methods  First, centroidal Voronoi diagram, which is generated by considering the topographic relief as density function, is used to simplify the terrain adaptively. Second, the terrain is reconstructed according to the sites of the centroidal Voronoi diagram that distributed in areas with large topographic relief, and Voronoi vertices, which mostly distributed on the terrain feature lines. Finally, the effect of simplification is verified by contrasting the feature lines of the original terrain and the reconstructed terrain. And the accuracy of the proposed algorithm is compared with that of 3D Douglas-Peucker algorithm.
      Results  Feature lines, such as ridge lines, valley lines and contours, extracted from simplified terrain and the original terrain have high degree of overlap. Therefore, the proposed algorithm maintains the features of the original terrain well. The simplification error of the proposed algorithm is lower than that of the 3D Douglas-Peucker algorithm at the same level of simplification.
      Conclusions  The proposed algorithm has a higher accuracy compared with 3D Douglas-Peucker algorithm.

     

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