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首页> 外文期刊>International journal of image and data fusion >An assessment of the efficiency of spatial distances in linear object matching on multi-scale, multi-source maps
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An assessment of the efficiency of spatial distances in linear object matching on multi-scale, multi-source maps

机译：多尺度，多源地图上线性物体匹配中空间距离效率的评估

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摘要

Distance in geospatial sciences has many applications, including the calculation of spatial similarity degree in object-matching problems. Various distances have so far been utilised for this purpose. However, no study has examined the efficiency of methods used for finding solutions for linear object matching in data sets with different or the same scales and sources. The present study investigated the efficiency of the most important and applicable spatial distances (13 types of distance methods) in vector data sets with different scales and sources. To this end, we employed three data sets of urban roads network of different sources with the scales of 1:2000, 1:5000 and 1:25,000. In the considered approach, the data sets are initially pre-processed to unify the format and coordinate system as well as removing topological errors. The corresponding objects in the data sets are then identified, and one-to-null, null-to-one, one-to-one, one-to-many, many-to-one and many-to-many relations are extracted. Ultimately, the method with the minimum dispersion in calculation of the distances between corresponding objects is selected as the efficient method. The results indicated that the short-line median and mean Hausdorff methods achieved higher efficiencies compared to the other employed methods. In addition to achieving a smaller variance compared to other introduced methods, these two methods are well capable of identifying one-to-many (many-to-one) and many-to-many relations.
机译：地理空间科学中的距离具有许多应用，包括对象匹配问题中空间相似度的计算。迄今为止，各种距离已被用于此目的。但是，没有研究检查过用于在具有不同或相同比例和来源的数据集中寻找线性对象匹配解决方案的方法的效率。本研究调查了具有不同比例和来源的矢量数据集中最重要和适用的空间距离（13种距离方法）的效率。为此，我们采用了三个不同来源的城市道路网络数据集，其比例分别为1：2000、1：5000和1：25,000。在考虑的方法中，首先对数据集进行预处理，以统一格式和坐标系以及消除拓扑错误。然后识别数据集中的对应对象，并提取一对零，空对一，一对一，一对多，多对一和多对多的关系。最终，选择在计算相应对象之间的距离时具有最小离散的方法作为有效方法。结果表明，与其他采用的方法相比，短线中位数和均值Hausdorff方法具有更高的效率。与其他引入的方法相比，除了实现较小的方差外，这两种方法还能够识别一对多（多对一）和多对多关系。

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