On a knowledge-based approach to the classification of mobile laser scanning point clouds

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    Abstract

    A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79% for the Paris-rue-Madame point cloud bench mark data set.

    Original languageEnglish
    Pages (from-to)411-416
    Number of pages6
    JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    Volume42
    Issue number4
    DOIs
    Publication statusPublished - 2018
    EventISPRS TC IV Mid-term Symposium “3D Spatial Information Science – The Engine of Change” - Delft, Netherlands
    Duration: 1 Oct 20185 Oct 2018

    Keywords

    • Classification
    • Feature extraction
    • Knowledge-based system
    • Mobile laser scanning
    • Point clouds

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