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中文摘要：
从分析人在寻找陌生地物时的思维习惯入手,在居民地匹配过程中引入了空间关系相似性约束。对两个居民地之间的拓扑关系、距离关系和方向关系的相似性进行了分析,并提出了符合人认知习惯的离散化计算方法。在匹配过程中,以突出居民地作为起始对象,以已匹配居民地作为参照,对未匹配居民地按空间邻近原则进行广度优先搜索,利用空间关系相似性约束来缩小匹配目标备选集,实现了空间关系有序的精确匹配。最后,对已匹配对象实施了基于邻近对象空间关系相似性校验的匹配质量检查。实验结果表明,该算法在待匹配数据位移较大、居民地对象形状同质化较高的情况下优势明显,能够有效提升匹配精度。
英文摘要：
Settlement matching is one of the kernel parts of multi-source spatial data fusion and multi-scale data updating. Following the cognition habits of mankind in finding strange buildings, the spatial relationship similarity is used to assist the settlement matching process. The discrete computing method according with human cognitive habits is proposed after analyzed the similarity of topological relationship, distance relationship and direction relationship. And the matching processes are as fellows. Firstly, the outstanding settlement of the original object is picked up and computed to find its matching object. Secondly, refer- encing the matched object, the next matching object is achieved by the extend-first traversal to unmatched objects. Thirdly, the precision matching is fulfilled by traversing every settle- ment object all in this way. Finally, the matching quality is evaluated by comparing the spa- tial relationship similarity of adjacent objects. Test illustrates that this method can effective- ly improve the matching precision in the case of data hardly displacement and high settlement shape homogeneity.
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