张慧杰, 吕英华, 刘淑华. 一种基于自适应区域分割的地形模型简化方法[J]. 计算机研究与发展, 2010, 47(1): 53-61.
 引用本文: 张慧杰, 吕英华, 刘淑华. 一种基于自适应区域分割的地形模型简化方法[J]. 计算机研究与发展, 2010, 47(1): 53-61.
Zhang Huijie, Lü Yinghua, Liu Shuhua. A Terrain Model Simplification Method Based on Adaptive Areas Division[J]. Journal of Computer Research and Development, 2010, 47(1): 53-61.
 Citation: Zhang Huijie, Lü Yinghua, Liu Shuhua. A Terrain Model Simplification Method Based on Adaptive Areas Division[J]. Journal of Computer Research and Development, 2010, 47(1): 53-61.

## A Terrain Model Simplification Method Based on Adaptive Areas Division

• 摘要: 鉴于统一误差计算模型简化方法自适应性差的问题，提出了一种根据地势特征自适应分割地形区域从而有选择性地进行误差计算和模型简化的方法.针对地形模型数据量大的特点，建立了细节层次结构，并证明了空间快速索引方法的有效性.为了解决平缓地势区域分割困难的问题，提出了基于凸点和扩散点相结合的特征选择算法，并有效控制了特征点的密度.在此基础上提出了多分辨率邻域节点查找与匹配方法，实现了区域的快速粗粒度分割. 提出了地表起伏度计算方法，进一步评价了分割区域的地势特征，从而对部分区域进行细分处理.在真实数据上进行了实验研究，结果表明算法性能以及简化模型的精度和自适应性较好.

Abstract: In view of the problem of the approximation models with poor self-adaptability by uniform error threshold, a method is proposed in this paper. In this method, the terrain is divided into some different areas with various terrain features, and then the error function and the error threshold are adaptively determined according to these areas. Aimed at the character of big data in terrain model, a detailed hierarchy is constructed and its fast index method is described. In order to control the density of feature points in the primary step, the feature points are selected based on the convex terrain and its diffused points. It solves the problem of the feature selection for the gentle slope terrain. The method of searching for multi-resolution adjacent nodes is put forward to accelerate the area division process with coarse grain, and the matching function between the adjacent nodes is also described. Besides, a relief degree function for terrain surface is proposed to evaluate the terrain areas, so as to determine the division strategy of the approximation model. As a result, the terrain model is of higher adaptability and precision. The experiments on some real data show that the method is superior to the uniform error threshold method.

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