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中文摘要：
现有多源同比例尺道路网匹配方法中，大多只利用道路自身特征进行匹配，而较少顾及道路周边要素对匹配过程的影响和约束，从而影响了道路网匹配效果的进一步提高，特别是对系统误差改正后仍存在一定位置或旋转偏差的道路数据进行匹配时，这种影响尤为明显。本文借鉴人类对陌生环境的空间认知特点，提出了一种顾及邻域居民地群组相似性的道路网匹配方法。该方法通过构建城市骨架线网确定与道路相邻的居民地群组，进而计算居民地群组空间关系和几何特征相似度来获得对应道路的匹配结果。其特点在于：对存在位置或旋转偏差的道路数据匹配，以其邻域空间内居民地群组的整体相似性指标来带动道路自身匹配，实际上是增加了周边居民地群组对道路匹配过程的约束，更具鲁棒性。试验及对比分析表明，本方法能够较好地解决系统误差改正后仍存在较大位置和旋转偏差的道路数据间的匹配问题，提高匹配的正确率。
英文摘要：
The existing matching methods for the multi-source road data of the same scale mainly consider the characteristics of the road itself, while the effect of the neighborhood features on matching process is generally ignored, which may restrict the further improvement of the matching results. This restriction can be more obvious for the matching data in which the location or rotation differences still exist after the system error rectification. A road network matching method that takes the similarity of the roads＇ neighborhood habitation cluster into consideration is proposed, which draws on the experience of the human spatial cognitive characteristics for the unfamiliar environment. Firstly, the neighborhood habitation cluster of the road is extracted by the urban skeleton line network~ Then by calculating the spatial relation similarity and geometry characteristic similarity of the neighborhood habitation cluster, the matching results can be obtained. The advantage of this method is that for the road data which have obvious location or rotation differences, the similarity of their neighborhood habitation clusters can be treated as a proper matching index. Actually, roads＇ neighborhood habitation cluster can be a constraint of the road matching process and enhance its robustness. The tests and comparison analysis indicate that this method can solve the matching problems of the road data which still have obvious location or rotation differences after system error rectification and improve the matching correctness.
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