Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data Article Swipe
Ehsan Farahbakhsh
,
Rohitash Chandra
,
Hugo K.H. Olierook
,
R. Scalzo
,
Chris Clark
,
Steven M. Reddy
,
R. Dietmar Müller
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1080/01431161.2019.1674462
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1080/01431161.2019.1674462
The extraction of geological lineaments from digital satellite data is a\nfundamental application in remote sensing. The location of geological\nlineaments such as faults and dykes are of interest for a range of\napplications, particularly because of their association with hydrothermal\nmineralization. Although a wide range of applications have utilized computer\nvision techniques, a standard workflow for application of these techniques to\nmineral exploration is lacking. We present a framework for extracting\ngeological lineaments using computer vision techniques which is a combination\nof edge detection and line extraction algorithms for extracting geological\nlineaments using optical remote sensing data. It features ancillary computer\nvision techniques for reducing data dimensionality, removing noise and\nenhancing the expression of lineaments. We test the proposed framework on\nLandsat 8 data of a mineral-rich portion of the Gascoyne Province in Western\nAustralia using different dimension reduction techniques and convolutional\nfilters. To validate the results, the extracted lineaments are compared to our\nmanual photointerpretation and geologically mapped structures by the Geological\nSurvey of Western Australia (GSWA). The results show that the best correlation\nbetween our extracted geological lineaments and the GSWA geological lineament\nmap is achieved by applying a minimum noise fraction transformation and a\nLaplacian filter. Application of a directional filter instead shows a stronger\ncorrelation with the output of our manual photointerpretation and known sites\nof hydrothermal mineralization. Hence, our framework using either filter can be\nused for mineral prospectivity mapping in other regions where faults are\nexposed and observable in optical remote sensing data.\n
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/01431161.2019.1674462
- OA Status
- green
- Cited By
- 54
- References
- 138
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2894899844Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1080/01431161.2019.1674462Digital Object Identifier
- Title
-
Computer vision-based framework for extracting tectonic lineaments from optical remote sensing dataWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-10-11Full publication date if available
- Authors
-
Ehsan Farahbakhsh, Rohitash Chandra, Hugo K.H. Olierook, R. Scalzo, Chris Clark, Steven M. Reddy, R. Dietmar MüllerList of authors in order
- Landing page
-
https://doi.org/10.1080/01431161.2019.1674462Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1810.02320Direct OA link when available
- Concepts
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Lineament, Prospectivity mapping, Remote sensing, Geology, Geologic map, Mineral exploration, Computer science, Tectonics, Geochemistry, Geomorphology, Seismology, Structural basinTop concepts (fields/topics) attached by OpenAlex
- Cited by
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54Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 6, 2023: 8, 2022: 16, 2021: 8Per-year citation counts (last 5 years)
- References (count)
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138Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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