| 基于图论的遥感影像误匹配点自动探测方法 |
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| 引用本文： | 袁修孝,袁巍,陈时雨.基于图论的遥感影像误匹配点自动探测方法[J].武汉大学学报(信息科学版),2018,43(12):1854-1860. |
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| 作者姓名： | 袁修孝 袁巍 陈时雨 |
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| 作者单位： | 1.武汉大学遥感信息工程学院, 湖北 武汉, 430079 |
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| 基金项目： | 国家自然科学基金41771479国家高分专项（民用部分）50-H31D01-0508-13/15国家973计划2012CB719902 |
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| 摘 要： | 提出一种基于图论的卫星遥感影像误匹配点自动剔除方法。首先以稀疏匹配点为节点分别构建左右影像的完全图；然后利用每个节点所对应三角形的相似度之和为属性值构建导出图；最后通过搜索导出图中属性值最小的节点定位误匹配点。在剔除单个误匹配点的基础上，采用重新构建完全图-导出图-定位误匹配点的循环搜索策略，达到自动探测并剔除多个误匹配点的目的。实验表明，该方法无需建立匹配点间的映射模型，仅利用三角形的相似关系就可以定位误匹配点，与广泛使用的随机抽样一致性粗差探测方法相比，对误匹配点具有更高的识别率和更低的误判率。
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| 关 键 词： | 影像匹配 误匹配点探测 图论 随机抽样一致性 匹配支持度 |
| 收稿时间： | 2018-05-08 |
An Automatic Detection Method of Mismatching Points in Remote Sensing Images Based on Graph Theory |
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| Affiliation: | 1.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China2.Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China3.Center for Spatial Information Science, University of Tokyo, Tokyo 277-8568, Japan4.School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China |
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| Abstract: | A graph theory-based mismatching detection method is proposed in this paper. At first, two complete graphs are constructed by the correspondences in left and right images, respectively. Then an induced graph is constructed by using the sum of similarity of the triangles corresponding to each node in complete graphs as the attribute value. Finally, the induced graph is refined by removing the node of which the attribute value is the smallest in the graph. In order to automatically locate multiple mismatching points, the graph theory-based mismatching elimination method is a recursive process. The whole process scheme is as follows, complete graph building, induced graph building, and mismatching point locating. The experimental results demonstrates that the accurate mapping model between matched points is not necessary in our mismatching detection method, while the local simila-rity of triangles is sufficient for locating the mismatching points. In addition, the true positive rate is higher and the false positive rate is lower compared to classical RANSAC (random sample consensus) bundler detection method. |
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