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    Processing tree point clouds using Gaussian Mixture Models

    Access Status
    Open access via publisher
    Authors
    Belton, David
    Moncrieff, Simon
    Chapman, Jane
    Date
    2013
    Type
    Conference Paper
    
    Metadata
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    Citation
    Belton, David and Moncrieff, Simon and Chapman, Jane. 2013. Processing tree point clouds using Gaussian Mixture Models, in Scaioni, M. and Lindenbergh, R.C. and Oude Elberink, S. and Schneider, D. and Pirotti, F. (ed), International Society for Photogrammetry and Remote Sensing (ISPRS) Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, Nov 11-13 2013, II-5/W2: pp. 43-48. Antalya, Turkey: ISPRS.
    Source Title
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2
    Source Conference
    2013 ISPRS Workshop Laser Scanning 2013
    DOI
    10.5194/isprsannals-II-5-W2-43-2013
    ISSN
    2194-9042
    URI
    http://hdl.handle.net/20.500.11937/10188
    Collection
    • Curtin Research Publications
    Abstract

    While traditionally used for surveying and photogrammetric fields, laser scanning is increasingly being used for a wider range of more general applications. In addition to the issues typically associated with processing point data, such applications raise a number of new complications, such as the complexity of the scenes scanned, along with the sheer volume of data. Consequently, automated procedures are required for processing, and analysing such data. This paper introduces a method for modelling multi-modal, geometrically complex objects in terrestrial laser scanning point data; specifically, the modelling of trees. The model method comprises a number of geometric features in conjunction with a multi-modal machine learning technique. The model can then be used for contextually dependent region growing through separating the tree into its component part at the point level. Subsequently object analysis can be performed, for example, performing volumetric analysis of a tree by removing points associated with leaves. The workflow for this process is as follows: isolate individual trees within the scanned scene, train a Gaussian mixture model (GMM), separate clusters within the mixture model according to exemplar points determined by the GMM, grow the structure of the tree, and then perform volumetric analysis on the structure.

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