矢量化多边形优化方法

从地理空间数据不确定性理论出发,提出了矢量化多边形的优化方法。该方法通过对矢量多边形进行"磨角插值"和基于可接受域限定的自适应滤波平滑处理,以此来削弱由空间数据表示不确定性造成的误差,使矢量化多边形最大限度地逼近地理实体真实轮廓。采用基于全局阈值的DP算法进行压缩处理,消除了数据冗余。从压缩比、位置误差及处理效率3个方面进行对比实验,验证了所提方法的有效性。

Optimization of Polygon Represented by Vector

Based on the uncertainty theory of geospatial data,an optimization method of vector polygon is proposed.In this method,vector polygons are smoothed by "grinding angle and interpolation" and adaptive filtering based on the acceptable domain,so as to weaken the errors caused by spatial data representation uncertainty and to maximize the approximation ofvector polygons to the true contours of geographic entities.The DP algorithm based on the global threshold is used to compress data which eliminates data redundancy.The effectiveness of the proposed method is verified from three aspects which are compression ratio,position error and processing efficiency.