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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Classification of Urban Point Clouds: A Robust Supervised Approach With Automatically Generating Training Data
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Classification of Urban Point Clouds: A Robust Supervised Approach With Automatically Generating Training Data

机译：城市点云分类：一种带有自动生成训练数据的鲁棒监督方法

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摘要

To reduce the cost of manually annotating training data for parsing outdoor scenes, we propose a supervised approach with automatically generating training data for classifying 3-D point clouds of large-scale urban scenes. In this approach, the input point cloud is aggregated into point clusters, and the disjoint set union issue is combined with geometric attributes of each point cluster to obtain object segments. The prior knowledge among different classes is used to label the segments by using the decision-tree model. Then, the initialized training samples are generated automatically. The confidence estimation for the labeling is employed to filter the mislabeled training samples. With the generated training data, we train a random forest classifier to create the initial classification of the 3-D scene on the set of descriptors for each 3-D point. The classification results are further optimized by multilabel conditional Random Fields. Experimental results on five urban point clouds captured by different types of scanners (i.e., terrestrial laser scanning, vehicle laser scanning, and airborne laser scanning datasets) demonstrate that the proposed approach achieves a competitive classification performance.
机译：为了减少人工注释训练数据来解析室外场景的成本，我们提出了一种监督方法，该方法可以自动生成训练数据以对大型城市场景的3D点云进行分类。在这种方法中，将输入点云聚合到点簇中，并将不相交集合并集问题与每个点簇的几何属性结合起来以获得对象段。通过使用决策树模型，可以使用不同类别之间的先验知识来标记细分。然后，自动生成初始化的训练样本。标记的置信度估计用于过滤标记错误的训练样本。利用生成的训练数据，我们训练一个随机森林分类器，以在每个3D点的描述符集上创建3D场景的初始分类。分类结果通过多标签条件随机字段进一步优化。通过不同类型的扫描仪（即地面激光扫描，车辆激光扫描和机载激光扫描数据集）捕获的五个城市点云的实验结果表明，该方法实现了竞争性的分类性能。

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  • 作者

    Zhuqiang Li; Liqiang Zhang; Ruofei Zhong; Tian Fang; Liang Zhang; Zhenxin Zhang;

  • 作者单位

    Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, and the State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, China;

    Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, and the State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, China;

    Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, and the State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, China;

    Hong Kong University of Science and Technology, Kowloon, Hong Kong;

    Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, and the State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, China;

    Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, and the State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, China;

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  • 正文语种 eng
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  • 关键词

    Three-dimensional displays; Training data; Laser radar; Silicon; Urban areas; Support vector machines; Training;

    机译：三维显示器;培训数据;激光雷达;硅;城市区域;支持向量机;培训;

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