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  • Building detection in complex scenes thorough effective separation of buildings from trees

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    Version of Record (VoR)
    Author(s)
    Awrangjeb, Mohammad
    Zhang, Chunsun
    Fraser, Clive S
    Griffith University Author(s)
    Awrangjeb, Mohammad
    Year published
    2012
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    Abstract
    Effective separation of buildings from trees is a major challenge in image-based automatic building detection. This paper presents a three-step method for effective separation of buildings from trees using aerial imagery and lidar data. First, it uses cues such as height to remove objects of low height such as bushes, and width to exclude trees with small horizontal coverage. The height threshold is also used to generate a ground mask where buildings are found to be more separable than in so-called normalized DSM. Second, image entropy and color information are jointly applied to remove easily distinguishable trees. Finally, ...
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    Effective separation of buildings from trees is a major challenge in image-based automatic building detection. This paper presents a three-step method for effective separation of buildings from trees using aerial imagery and lidar data. First, it uses cues such as height to remove objects of low height such as bushes, and width to exclude trees with small horizontal coverage. The height threshold is also used to generate a ground mask where buildings are found to be more separable than in so-called normalized DSM. Second, image entropy and color information are jointly applied to remove easily distinguishable trees. Finally, an innovative rule-based procedure is employed using the edge orientation histogram from the imagery to eliminate false positive candidates. The improved building detection algorithm has been tested on different test areas and it is shown that the algorithm offers high building detection rate in complex scenes which are hilly and densely vegetated.
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    Journal Title
    Photogrammetric Engineering and Remote Sensing
    Volume
    78
    Issue
    7
    Publisher URI
    https://www.asprs.org/pe-rs-past-issues/2012-pers-journals.html
    Copyright Statement
    © 2012 ASPRS. Reprinted with permission from the American Society for Photogrammetry & Remote Sensing, Bethesda, Maryland, www.asprs.org. Please refer to the journal's website for access to the definitive, published version.
    Subject
    Computer vision
    Image processing
    Geomatic engineering
    Photogrammetry and remote sensing
    Publication URI
    http://hdl.handle.net/10072/172519
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    • Journal articles

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