Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data
© 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, part...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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TAYLOR & FRANCIS LTD
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/77059 |
| _version_ | 1848763810328870912 |
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| author | Farahbakhsh, E. Chandra, R. Olierook, Hugo Scalzo, R. Clark, Chris Reddy, Steven Müller, R.D. |
| author_facet | Farahbakhsh, E. Chandra, R. Olierook, Hugo Scalzo, R. Clark, Chris Reddy, Steven Müller, R.D. |
| author_sort | Farahbakhsh, E. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T11:09:22Z |
| format | Journal Article |
| id | curtin-20.500.11937-77059 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:09:22Z |
| publishDate | 2020 |
| publisher | TAYLOR & FRANCIS LTD |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-770592019-12-03T01:38:33Z Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data Farahbakhsh, E. Chandra, R. Olierook, Hugo Scalzo, R. Clark, Chris Reddy, Steven Müller, R.D. Science & Technology Technology Remote Sensing Imaging Science & Photographic Technology WESTERN-AUSTRALIA CAPRICORN OROGEN FLUID-FLOW COMPUTATIONAL APPROACH STRUCTURAL CONTROLS EDGE-DETECTION SOUTHERN ALPS PALSAR DATA U-PB SATELLITE © 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. 2020 Journal Article http://hdl.handle.net/20.500.11937/77059 10.1080/01431161.2019.1674462 English TAYLOR & FRANCIS LTD restricted |
| spellingShingle | Science & Technology Technology Remote Sensing Imaging Science & Photographic Technology WESTERN-AUSTRALIA CAPRICORN OROGEN FLUID-FLOW COMPUTATIONAL APPROACH STRUCTURAL CONTROLS EDGE-DETECTION SOUTHERN ALPS PALSAR DATA U-PB SATELLITE Farahbakhsh, E. Chandra, R. Olierook, Hugo Scalzo, R. Clark, Chris Reddy, Steven Müller, R.D. Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data |
| title | Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data |
| title_full | Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data |
| title_fullStr | Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data |
| title_full_unstemmed | Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data |
| title_short | Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data |
| title_sort | computer vision-based framework for extracting tectonic lineaments from optical remote sensing data |
| topic | Science & Technology Technology Remote Sensing Imaging Science & Photographic Technology WESTERN-AUSTRALIA CAPRICORN OROGEN FLUID-FLOW COMPUTATIONAL APPROACH STRUCTURAL CONTROLS EDGE-DETECTION SOUTHERN ALPS PALSAR DATA U-PB SATELLITE |
| url | http://hdl.handle.net/20.500.11937/77059 |