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  • Outlier detection and robust plane fitting for building roof extraction from LiDAR data

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    Dey431512-Accepted.pdf (10.54Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Dey, Emon Kumar
    Awrangjeb, Mohammad
    Stantic, Bela
    Griffith University Author(s)
    Awrangjeb, Mohammad
    Dey, Emon Kumar
    Stantic, Bela
    Year published
    2020
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    Abstract
    Individual roof plane extraction from Light Detection and Ranging (LiDAR) point-cloud data is a complex and difficult task because of unknown semantic characteristics and inharmonious behaviour of input data. Most of the existing state-of-the-art methods fail to detect small true roof planes with exact boundaries due to outliers, occlusions, complex building structures, and other inconsistent nature of LiDAR data. In this paper, we have presented an improved building detection and roof plane extraction method, which is less sensitive to the outliers and unlikely to generate spurious planes. For this, a robust outlier detection ...
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    Individual roof plane extraction from Light Detection and Ranging (LiDAR) point-cloud data is a complex and difficult task because of unknown semantic characteristics and inharmonious behaviour of input data. Most of the existing state-of-the-art methods fail to detect small true roof planes with exact boundaries due to outliers, occlusions, complex building structures, and other inconsistent nature of LiDAR data. In this paper, we have presented an improved building detection and roof plane extraction method, which is less sensitive to the outliers and unlikely to generate spurious planes. For this, a robust outlier detection algorithm has been proposed in this paper along with a robust plane-fitting algorithm based on M-estimator SAmple Consensus (MSAC) for detecting individual roof planes. Using two benchmark datasets (Australian and International Society for Photogrammetry and Remote Sensing benchmark) with different numbers of buildings and sizes, trees and point densities, we have evaluated the proposed method. Experimental results show that the method removes outliers and vegetation almost accurately and offers a high success rate in terms of completeness and correctness (between 80% and 100% per-object) for both roof plane extraction and building detection. In most of the cases, the proposed method shows above 90% correctness.
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    Journal Title
    International Journal of Remote Sensing
    Volume
    41
    Issue
    16
    DOI
    https://doi.org/10.1080/01431161.2020.1737339
    Copyright Statement
    This is an Author's Accepted Manuscript of an article published in the International Journal of Remote Sensing,41 (16), pp. 6325-6354, 09 Jun 2020, copyright Taylor & Francis, available online at: https://doi.org/10.1080/01431161.2020.1737339
    Subject
    Physical Geography and Environmental Geoscience
    Geomatic Engineering
    Science & Technology
    Remote Sensing
    Imaging Science & Photographic Technology
    3-D BUILDINGS
    Publication URI
    http://hdl.handle.net/10072/397432
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    • Journal articles

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