Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data

    Access Status
    Fulltext not available
    Authors
    Farahbakhsh, E.
    Chandra, R.
    Olierook, Hugo
    Scalzo, R.
    Clark, Chris
    Reddy, Steven
    Müller, R.D.
    Date
    2020
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Farahbakhsh, E. and Chandra, R. and Olierook, H.K.H. and Scalzo, R. and Clark, C. and Reddy, S.M. and Müller, R.D. 2020. Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data. International Journal of Remote Sensing. 41 (5): pp. 1760-1787.
    Source Title
    International Journal of Remote Sensing
    DOI
    10.1080/01431161.2019.1674462
    ISSN
    0143-1161
    Faculty
    Faculty of Science and Engineering
    School
    School of Earth and Planetary Sciences (EPS)
    URI
    http://hdl.handle.net/20.500.11937/77059
    Collection
    • Curtin Research Publications
    Abstract

    © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. The extraction of tectonic lineaments from digital satellite data is a fundamental application in remote sensing. The location of tectonic lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to tectonic lineament extraction is lacking. We present a framework for extracting tectonic lineaments using computer vision techniques. The proposed framework is a combination of edge detection and line extraction algorithms for extracting tectonic lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. The efficiency of two convolutional filters are compared in terms of enhancing the lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia. To validate the results, the extracted lineaments are compared to geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted tectonic lineaments and the GSWA tectonic lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter shows a strong correlation with known sites of hydrothermal mineralization. Hence, our method using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data.

    Related items

    Showing items related by title, author, creator and subject.

    • Introduction to the issue on multitarget tracking
      Mallick, M.; Vo, Ba-Ngu; Kirubarajan, T.; Arulampalam, S. (2013)
      Multitarget tracking has a long history spanning over 50 years and it refers to the problem of jointly estimating the number of targets and their states from sensor data. Today, multitarget tracking has found applications ...
    • Reducing the dimensionality of hyperspectral remotely sensed data with applications for maximum likelihood image classification
      Santich, Norman Ty (2007)
      As well as the many benefits associated with the evolution of multispectral sensors into hyperspectral sensors there is also a considerable increase in storage space and the computational load to process the data. ...
    • Algebraic method to speed up robust algorithms: example of laser-scanned point clouds
      Paláncz, B.; Awange, Joseph; Lovas, T.; Lewis, R.; Molnár, B.; Heck, B.; Fukuda, Y. (2016)
      Surface reconstruction from point clouds generated by laser scanning technology has become a fundamental task in many fields of geosciences, such as robotics, computer vision, digital photogrammetry, computational geometry, ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.