一种形状多级描述方法及在多尺度空间数据几何相似性度量中的应用

A Shape Multilevel Description Method and Application in Measuring Geometry Similarity of Multi-scale Spatial Data

利用多级弦长函数和中心距离函数从全局整体到局部细节逐级描述几何形状,建立通用多尺度空间数据几何相似性度量模型。基于高斯概率统计模型改进传统的Hausdorff距离,引入信息检索中的相关反馈技术解决相似度量模型中各指标阈值的确定问题。最后将相似度量模型分别应用于不同比例尺数据匹配和空间目标化简前后的相似度量,试验表明,基于该描述方法的相似度量模型可有效实现不同比例尺水域数据的匹配和相似度量。

A universal measure model of geometry similarity is established for multi-scale spatial data based on multilevel chord length functions and center distance functions.These functions can describe geometry shape from entirety to part gradually.The traditional Hausdorff distance is improved based on the statistic Gaussian mode.The enactment of every criteria threshold value in the measure model of geometry similarity is solved by introducing relevance feedback techniques.At last,the model is applied in data matching of different scales and similarity measure of spatial object simplification.Experiments show that the model can realize the matching and similarity measure effectively in waters data of different scales.