基于分形维数的多尺度面目标匹配对相似性度量

Multi-scale polygon entities matched pairs' similarity measuring based on fractal dimension

多尺度空间数据联动更新技术已经成为提高地图数据现势性的一种重要手段,为了解决该技术中目标匹配结果的质量评定问题,重点针对其中的面状要素,提出了一种基于分形维数的地图数据面实体相似性度量方法。基于分形维数的多尺度面目标相似性度量方法,以分形维数来刻画面目标的几何形状特征,结合其空间位置、大小和分布模式等信息,对多尺度同名面目标之间的相似性进行定量度量。该方法既考虑了面目标的局部结构特征,又兼顾了面目标的整体分布特性,还具有旋转、平移和尺度不变的特性。最后以某地居民地数据匹配对为试验数据进行试验,并通过与紧致度形状描述方法进行比较,结果表明,该方法在面目标相似性度量方面具有很好的实用效果,为多尺度地图面目标匹配结果评价提供了一种有效手段。

Multi-scale spatial data linkage updating technology has become an important way to improve the data instantaneity. In order to solve the problem of the quality evaluation of the target matching result and focus on the polygon elements, a similarity measure method based on fractal dimension for map dataset is proposed. Fractal dimension based multi scale polygon elements similarity measure method uses the fractal dimension to describe the geo- metric features of the polygon elements, and combines its spatial position, size and distribu tion mode to quantify the similarity between multi-scale polygon elements with the same name. This method not only takes into account the local structure of the polygon elements, but also takes into account the global distribution of the polygon elements. It also has the characteristics of rotation, translation and scaling. We test the experimental data by compa ring the data of the residents in a certain place and compare it with the shape description method of tightness. The experimental results show that this method has a good practical effect in the aspect similarity measure, which provides an effective method to evaluate the matching results of the target on multi scale.