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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Discriminative-Dictionary-Learning-Based Multilevel Point-Cluster Features for ALS Point-Cloud Classification
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Discriminative-Dictionary-Learning-Based Multilevel Point-Cluster Features for ALS Point-Cloud Classification

机译：基于歧视性词典学习的多层点集群功能，用于ALS点云分类

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摘要

Efficient presentation and recognition of on-ground objects from airborne laser scanning (ALS) point clouds are a challenging task. In this paper, we propose an approach that combines a discriminative-dictionary-learning-based sparse coding and latent Dirichlet allocation (LDA) to generate multilevel point-cluster features for ALS point-cloud classification. Our method takes advantage of the labels of training data and each dictionary item to enforce discriminability in sparse coding during the dictionary learning process and more accurately further represent point-cluster features. The multipath AdaBoost classifiers with the hierarchical point-cluster features are trained, and we apply them to the classification of unknown points by the heritance of the recognition results under different paths. Experiments are performed on different ALS point clouds; the experimental results have shown that the extracted point-cluster features combined with the multipath classifiers can significantly enhance the classification accuracy, and they have demonstrated the superior performance of our method over other techniques in point-cloud classification.
机译：机载激光扫描（ALS）点云对地面物体的有效表示和识别是一项艰巨的任务。在本文中，我们提出了一种基于判别词典学习的稀疏编码和潜在狄利克雷分配（LDA）相结合的方法，以生成用于ALS点云分类的多级点簇特征。我们的方法利用训练数据的标签和每个字典项的标签来在字典学习过程中的稀疏编码中实现可区分性，并更准确地表示点集群特征。训练了具有分层点聚类功能的多路径AdaBoost分类器，我们通过在不同路径下识别结果的遗传将它们应用于未知点的分类。实验是在不同的ALS点云上进行的；实验结果表明，提取的点聚类特征与多径分类器相结合可以显着提高分类精度，并且证明了我们的方法在点云分类中优于其他技术。

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  • 来源
    《IEEE Transactions on Geoscience and Remote Sensing》 |2016年第12期|7309-7322|共14页
  • 作者

    Zhenxin Zhang; Liqiang Zhang; Xiaohua Tong; Bo Guo; Liang Zhang; Xiaoyue Xing;

  • 作者单位

    State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, China;

    State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, China;

    School of Surveying and Geo-informatics, Tongji University, Shanghai, China;

    Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation and Shenzhen Key Laboratory of Spatial Smart Sensing, Shenzhen University, Shenzhen, China;

    State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, China;

    State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, China;

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  • 正文语种 eng
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  • 关键词

    Three-dimensional displays; Dictionaries; Feature extraction; Encoding; Shape; Lasers; Image segmentation;

    机译：三维显示;字典;特征提取;编码;形状;激光;图像分割;

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