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    Multispectral analysis of indigenous rock art using terrestrial laser scanning

    Access Status
    Open access via publisher
    Authors
    Skoog, B.
    Helmholz, Petra
    Belton, D.
    Date
    2016
    Type
    Conference Paper
    
    Metadata
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    Citation
    Skoog, B. and Helmholz, P. and Belton, D. 2016. Multispectral analysis of indigenous rock art using terrestrial laser scanning, pp. 405-412.
    Source Title
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
    DOI
    10.5194/isprsarchives-XLI-B5-405-2016
    ISSN
    1682-1750
    School
    Department of Spatial Sciences
    URI
    http://hdl.handle.net/20.500.11937/45140
    Collection
    • Curtin Research Publications
    Abstract

    Multispectral analysis is a widely used technique in the photogrammetric and remote sensing industry. The use of Terrestrial Laser Scanning (TLS) in combination with imagery is becoming increasingly common, with its applications spreading to a wider range of fields. Both systems benefit from being a non-contact technique that can be used to accurately capture data regarding the target surface. Although multispectral analysis is actively performed within the spatial sciences field, its extent of application within an archaeological context has been limited. This study effectively aims to apply the multispectral techniques commonly used, to a remote Indigenous site that contains an extensive gallery of aging rock art. The ultimate goal for this research is the development of a systematic procedure that could be applied to numerous similar sites for the purpose of heritage preservation and research. The study consisted of extensive data capture of the rock art gallery using two different TLS systems and a digital SLR camera. The data was combined into a common 2D reference frame that allowed for standard image processing to be applied. An unsupervised k-means classifier was applied to the multiband images to detect the different types of rock art present. The result was unsatisfactory as the subsequent classification accuracy was relatively low. The procedure and technique does however show potential and further testing with different classification algorithms could possibly improve the result significantly.

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