{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/computer-vision-based-framework-for","title":"Computer vision-based framework for extracting geological lineaments from optical remote sensing data","arxiv_id":"1810.02320","date":"2018-10-04","proceeding":null,"authors":["Ehsan Farahbakhsh","Rohitash Chandra","Hugo K. H. Olierook","Richard Scalzo","Chris Clark","Steven M. Reddy","R. Dietmar Muller"],"abstract":"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.","url_abs":"http://arxiv.org/abs/1810.02320v1","url_pdf":"http://arxiv.org/pdf/1810.02320v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"computer-vision-based-framework-for","repo_url":"https://github.com/intelligent-exploration/IP_MinEx","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"edge-detection","task_name":"Edge Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}