| 利用目标区域拓扑关系图提取建筑物点云 |
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| 引用本文： | 黄荣刚,杨必胜,李健平,田茂,梁新美.利用目标区域拓扑关系图提取建筑物点云[J].武汉大学学报(信息科学版),2017,42(4):475-481. |
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| 作者姓名： | 黄荣刚 杨必胜 李健平 田茂 梁新美 |
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| 作者单位： | 1.武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉, 430079 |
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| 基金项目： | 国家科技支撑计划No. 2014BAL05B07国家自然科学基金No. 41531177海洋公益性行业科研专项经费No. 2013418025-6 |
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| 摘 要： | 建筑物提取一直是机载激光点云数据处理研究的热点，其中建筑物和其他地物之间的区分是研究的核心和难点。为提高建筑物与其他地物在机载激光点云中的区分能力，提出了一种建筑物点云层次提取方法。首先，在点云滤波后，从非地面点云中提取建筑物候选区域；然后，通过形态学重建和点云平面分割方法对建筑物候选区域构建多尺度空间，并建立目标区域的拓扑关系图；最后，在拓扑关系图基础上，利用5种特征量对目标区域分类，并精确提取建筑物点云。为了测试算法的有效性和可靠性，利用国际摄影测量与遥感学会（International Society for Photogrammetry and Remote Sensing，ISPRS）提供的Vaihingen和Toronto两组测试数据集进行实验，并由ISPRS对结果进行评估，其中基于面积和目标的完整度、正确率和提取质量分别都大于87.8%、94.7%、87.3%。与其他建筑物提取方法相比，该方法在基于面积和目标的质量指标方面最为稳定。实验结果表明，在不同的城市场景下，该算法能够稳健地提取建筑物，并保持很高的正确率。
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| 关 键 词： | 机载激光雷达 点云数据 形态学重建 点云分割 建筑物提取 拓扑关系图 |
| 收稿时间： | 2016-09-12 |
Building Points Detection from Airborne LiDAR Point Clouds Using Topological Relationship Graph Within Each Object Region |
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| Institution: | 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China2.Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China |
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| Abstract: | In the field of Airborne LiDAR point clouds processing, building points extraction is always an active area. The core of this task is to separate building points from vegetation or other objects, but it is very difficult in different urban scenes. Therefore, this paper proposes a hierarchical method to precisely detect building points, aiming to improve the ability of separating buildings and other objects in various complex urban scenes. The method firstly separates non-ground points from ground points based on the filtering process, and to extract building candidate regions from non-ground points according to some simple geometrical features of a building. For each building candidate region, the morphological reconstruction and the point segmentation are fused to generate multi-scale space, and to construct topological relationship graphs between adjacent scales. Then, the proposed method extracts building regions from all object regions by five features based on topological relationship graphs. Finally, the proposed method removes non-building points from building regions for obtaining the final building points. In order to verify the validity and reliability of the proposed method, ISPRS (International Society for Photogrammetry and Remote Sensing) benchmark datasets from Vaihingen and Toronto are selected to perform experiments, and the results are evaluated by ISPRS. Results show that Completeness, Correctness, and Quality of object-based or area-based are larger than 87.8%, 94.7% and 87.3%, respectively. And compared with other methods, the method is the most robust in object-based or area-based evaluation. It demonstrates that the method could robustly detect building points in different urban scenes with a high correctness. |
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