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

Abstract

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.