An adaptive end-to-end classification approach for mobile laser scanning point clouds based on knowledge in urban scenes

Mingxue Zheng, Huayi Wu, Yong Li

    Research output: Contribution to journalArticleScientificpeer-review

    4 Citations (Scopus)
    92 Downloads (Pure)

    Abstract

    It is fundamental for 3D city maps to efficiently classify objects of point clouds in urban scenes. However, it is still a large challenge to obtain massive training samples for point clouds and to sustain the huge training burden. To overcome it, a knowledge-based approach is proposed. The knowledge-based approach can explore discriminating features of objects based on people's understanding of the surrounding environment, which exactly replaces the role of training samples. To implement the approach, a two-step segmentation procedure is carried out in this paper. In particular, Fourier Fitting is applied for second adaptive segmentation to separate points of multiple objects lying within a single group of the first segmentation. Then height difference and three geometrical eigen-features are extracted. In comparison to common classification methods, which need massive training samples, only basic knowledge of objects in urban scenes is needed to build an end-to-end match between objects and extracted features in the proposed approach. In addition, the proposed approach has high computational efficiency because of no heavy training process. Qualitative and quantificational experimental results show the proposed approach has promising performance for object classification in various urban scenes.

    Original languageEnglish
    Article number186
    Number of pages21
    JournalRemote Sensing
    Volume11
    Issue number2
    DOIs
    Publication statusPublished - 2019

    Keywords

    • 3D city maps
    • Fourier fitting
    • Geometrical eigen-features
    • Knowledge
    • Point cloud

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