| 基于邻近模式的多比例尺居民地松弛迭代匹配 |
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| 引用本文： | 张云菲,黄金彩,邓敏,房晓亮,胡继萍.基于邻近模式的多比例尺居民地松弛迭代匹配[J].武汉大学学报(信息科学版),2018,43(7):1098-1105. |
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| 作者姓名： | 张云菲 黄金彩 邓敏 房晓亮 胡继萍 |
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| 作者单位： | 1.长沙理工大学交通运输工程学院, 湖南 长沙, 410114 |
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| 基金项目： | 国家自然科学基金41601495国家自然科学基金41471385国家自然科学基金41501442中国博士后科学基金2015M582345测绘遥感信息工程国家重点实验开放基金17S01 |
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| 摘 要： | 空间目标匹配是实现多源空间信息融合、空间对象变化检测与动态更新的重要前提。针对多比例尺居民地匹配问题，提出了一种基于邻近模式的松弛迭代匹配方法。该方法首先利用缓冲区分析与空间邻近关系检测候选匹配目标与邻近模式，同时计算候选匹配目标或邻近模式间的几何相似性得到初始匹配概率矩阵；然后对邻近候选匹配对进行上下文兼容性建模，利用松弛迭代方法求解多比例尺居民地的最优匹配模型，选取匹配概率最大并满足上下文一致的候选匹配目标或邻近模式为最终匹配结果。实验结果表明，所提出的多比例尺居民地匹配方法具有较高的匹配精度，能有效克服形状轮廓同质化与非均匀性偏差问题，并准确识别1:M、M:N的复杂匹配关系。
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| 关 键 词： | 居民地匹配 松弛迭代 邻近模式 多比例尺 空间数据集成 |
| 收稿时间： | 2017-01-05 |
Relaxation Labelling Matching for Multi-scale Residential Datasets Based on Neighboring Patterns |
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| Affiliation: | 1.School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China2.School of Geosciences and Info-physics, Central South University, Changsha 410083, China3.Zhongnan Engineering Corporation Limited, Power China, Changsha 410014, China |
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| Abstract: | This paper proposes a relaxation labelling matching approach for multi-scale residential datasets based on neighboring patterns. Firstly, we detect the candidate matching objects and neighboring patterns by buffering analysis and spatial neighboring relations. Secondly, the geometric similarities of candidate matching objects or neighboring patterns are calculated to initialize the matching matrix that contains 1:1, 1:M and M:N relations. After that, the contextual information of neighborhood objects or patterns are explored to heuristically update the matching matrix to achieve a global consistency. The matching pairs with maximum probabilities are finally selected after context consistency detection. The experimental results and contrast analysis show that our method obtains high correct matching rates, efficiently overcomes the problems of shape homogeneity and uneven deviation, and can correctly identify complex 1:M and M:N matching objects in multi-scale residential datasets. |
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