不同比例尺居民地数据的多因子加权匹配方法

Multi-factor Weighted Matching Method for ResidentialArea Data of Different Scales

随着多尺度地图数据的出现,不同领域对其需求不断增大,保持数据的现势性变得越来越重要。地图数据的跨尺度更新是地图更新的难点问题,包含跨尺度地图匹配、变化识别、数据更新等过程,其中地图匹配是保持地图更新的重要环节之一。研究提出了跨尺度居民地数据的多因子权重匹配方法,该方法利用距离、形状、大小、方向等空间相似性因子计算候选匹配对的各指标匹配概率,找出多种匹配关系,克服了存在于不同比例尺之间的位置偏差。通过实验验证,该方法比单因子的匹配方法具有更高的准确率。

Cross-scale updating of map data is a difficult problem in map updating,which includes cross-scale map matching,changing recognition,data updating and other processes.The principle of map matching is to find the corresponding relationship between the same object in the spatial data from different sources,so as to lay a foundation for change recognition.As an important element of map,inhabitant land is an important research object of map matching.In this paper,a multi-factor weight matching method for cross-scale residential area land data is studied.This method uses spatial similarity factors such as distance,shape,size and direction to calculate the matching probability of each index of candidate matching pairs,and finds out a variety of matching relationships,which can overcome the position deviation between different scales.Experimental results show that this method is more accurate than the single factor matching method.