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dc.contributor.authorFarahbakhsh, E.
dc.contributor.authorChandra, R.
dc.contributor.authorOlierook, Hugo
dc.contributor.authorScalzo, R.
dc.contributor.authorClark, Chris
dc.contributor.authorReddy, Steven
dc.contributor.authorMüller, R.D.
dc.date.accessioned2019-11-29T06:25:06Z
dc.date.available2019-11-29T06:25:06Z
dc.date.issued2020
dc.identifier.citationFarahbakhsh, 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.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/77059
dc.identifier.doi10.1080/01431161.2019.1674462
dc.description.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.

dc.languageEnglish
dc.publisherTAYLOR & FRANCIS LTD
dc.subjectScience & Technology
dc.subjectTechnology
dc.subjectRemote Sensing
dc.subjectImaging Science & Photographic Technology
dc.subjectWESTERN-AUSTRALIA
dc.subjectCAPRICORN OROGEN
dc.subjectFLUID-FLOW
dc.subjectCOMPUTATIONAL APPROACH
dc.subjectSTRUCTURAL CONTROLS
dc.subjectEDGE-DETECTION
dc.subjectSOUTHERN ALPS
dc.subjectPALSAR DATA
dc.subjectU-PB
dc.subjectSATELLITE
dc.titleComputer vision-based framework for extracting tectonic lineaments from optical remote sensing data
dc.typeJournal Article
dcterms.source.issn0143-1161
dcterms.source.titleInternational Journal of Remote Sensing
dc.date.updated2019-11-29T06:25:05Z
curtin.departmentSchool of Earth and Planetary Sciences (EPS)
curtin.accessStatusFulltext not available
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidOlierook, Hugo [0000-0001-5961-4304]
curtin.contributor.orcidReddy, Steven [0000-0002-4726-5714]
curtin.contributor.orcidClark, Chris [0000-0001-9982-7849]
curtin.contributor.researcheridReddy, Steven [A-9149-2008]
curtin.contributor.researcheridClark, Chris [B-6471-2008]
dcterms.source.eissn1366-5901
curtin.contributor.scopusauthoridOlierook, Hugo [55922674600]
curtin.contributor.scopusauthoridReddy, Steven [7402263354]
curtin.contributor.scopusauthoridClark, Chris [55240014000]


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