Object matching facilitates spatial data integration, updating, evaluation, and management. However, data to be matched often originate from different sources and present problems with regard to positional discrepancies and different levels of detail. To resolve these problems, this article designs an iterative matching framework that effectively combines the advantages of the contextual information and an artificial neural network. The proposed method can correctly aggregate one‐to‐many (1:N) and many‐to‐many (M:N) potential matching pairs using contextual information in the presence of positional discrepancies and a high spatial distribution density. This method iteratively detects new landmark pairs (matched pairs), based on the prior landmark pairs as references, until all landmark pairs are obtained. Our approach has been experimentally validated using two topographic datasets at 1:50 and 1:10k. It outperformed a method based on a back‐propagation neural network. The precision increased by 4.5% and the recall increased by 21.6%, respectively.

中文翻译：

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基于上下文信息的迭代方法，用于匹配多尺度多边形对象数据集

对象匹配有助于空间数据集成，更新，评估和管理。但是，要匹配的数据通常来自不同的来源，并且在位置差异和细节层次上存在问题。为了解决这些问题，本文设计了一个迭代匹配框架，该框架有效地结合了上下文信息和人工神经网络的优势。在存在位置差异和高空间分布密度的情况下，使用上下文信息，所提出的方法可以正确地聚合一对多（1：N）和多对多（M：N）潜在匹配对。该方法基于先前的地标对作为参考，迭代检测新的地标对（匹配对），直到获得所有地标对。我们的方法已经使用两个地形数据集以1:50和1：10k进行了实验验证。它的性能优于基于反向传播神经网络的方法。准确性分别提高了4.5％和召回率提高了21.6％。