Automatic lineament extraction from ASTER images using the Hough transform, JabalBarez 1:100000 sheet

Authors

1 School of Mining Engineering, College of Engineering, University of Tehran, Iran

2 Mining Department, Isfahan University of Technology, Isfahan, Iran

3 School of Mining, College of Engineering, University of Tehran, Tehran, Iran Department of Geoscience, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada

Abstract

The Jebal Barez area is situated in the Urmia-Dokhtar magmatic belt, and the porphyry copper mineralization in this region has close relationship with faults and granitoid masses. Determination of the structural lineaments is a good guide to identify the location of copper mineralization in the region. In this paper, an automatic algorithm was used to extract the lineaments from ASTER images. This algorithm can highly reduce user errors and runtime. In this paper, the CANNY algorithm was used as an edge-detector filter while Hough transform was used to extract linear features from satellite imagery. Finally, after investigating and detecting their operation, the faults associated with copper mineralization were recognized in the region. According the rose-diagram, two fault systems were highlighted that the main fault system is located along the NW-SE, and the fault system along the N-S boundary forms a small fraction of the total fractures in the region. the fault density map of the area shows a high potential for porphyry copper mineralization. In order to investigate the success of the ASTER images in extracting faults in the region these faults were compared with the faults of geology map, that 63.05% of them had overlapping with geology faults.

Keywords

References

 Argialas, D., Mavrantza, O.D., Polytechneiou H., 2004. Comparison of Edge Detection and Hough Transform Techniques for the Extraction of Geologic Features. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, p. 790–795.
Biswas, R., Sil, J., 2012. An Improved Canny Edge Detection Algorithm Based on Type-2 Fuzzy Sets. Procedia Technology, 4, 820–824. DOI:10.1016/j.protcy.2012.05.134.
Charnpratheep, K., Zhou, Q, Garner, B., 1997. Preliminary landfill site selecting using fuzzy geographical information systems. Waste Management & Research 15, 197–215.
Chowdary, V.M., Ramakrishnan, D., Srivastava, Y.K., Chandran. V., Jeyaram, A., 2009. Integrated water resource development plan for sustainable management of mayurakshi watershed, India using remote sensing and GIS. Water Resources Management 23, 1581–1602. DOI:10.1007/s11269-008-9342-9.
Cooper, S. M., 2010. Potential field Investigation of the Liberia Basin , West Africa. Journal of American Science 6, 199–207.
Duda, R. O., Hart. P.E., 1972. Use of the {H}ough transform to detect lines and cures in pictures. Communications of the Association Computing Machinery 15, 11–15. DOI:10.1145/361237.361242.
Fitton, N. C., Cox, S. J. D., 1998. Optimising the application of the Hough transform for automatic feature extraction from geoscientific images. Computers & Geosciences 24, 933–951. DOI:10.1016/S0098-3004(98)00070-3.
Karantzalos, K., Argialas, D., 2006. Improving edge detection and watershed segmentation with anisotropic diffusion and morphological levellings. International Journal of Remote Sensing 27, 5427–5434. DOI:10.1080/01431160600944010.
Karimpour, M. H., Malekzadeh Shafaroudi, A., 2016. Satellite data processing in order to identify sodium and calcium bentonite deposits in eastern Iran. Advanced Applied Geology 6, 84–96. DOI:10.22055/AAG.2016.12636.
Krishnamurthy, J., Venkatesa Kumar, N., Jayaraman, V., Manivel, M., 1996. An approach to demarcate ground water potential zones through remote sensing and a geographical information system. International Journal of Remote Sensing 17, 1867–1884. DOI:10.1080/01431169608948744.
Lee, M., Morris, W., Harris, J., Leblanc, G., 2012. An automatic network-extraction algorithm applied to magnetic. The Leading Edge. No. January. p. 26–31.
Masoud, A. A., Koike, K., 2011. Auto-detection and integration of tectonically significant lineaments from SRTM DEM and remotely-sensed geophysical data. ISPRS Journal of Photogrammetry and Remote Sensing 66, 818–832. DOI:10.1016/j.isprsjprs.2011.08.003.
Mukherjee, S., 1999. Microzonation of seismic and landslide prone areas for alternate highway alignment in a part of western coast of India using remote sensing techniques. Journal of the Indian Society of Remote Sensing 27, 81–90. DOI:10.1007/BF02990804.
Rahnama, M., Gloaguen, R., 2014. TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 1: Line Segment Detection and Extraction. Remote Sensing 6, 5938–5958. DOI:10.3390/rs6075938.
Sanjay, P.R., Naoghare, M.M., 2015. Review on Determination of Edges by Automatic Threshold Value Generation. International Journal of Computer Science and Mobile Computing 4, 58–66.
Şener, Ş., Sener, E., Karagüzel, R., 2011. Solid waste disposal site selection with GIS and AHP methodology: A case study in Senirkent-Uluborlu (Isparta) Basin, Turkey. Environmental Monitoring and Assessment 173, 533–554. DOI:10.1007/s10661-010-1403-x.
Wladis, D., 1999. Automatic Lineament Detection Using Digital Elevation Models with Second Derivative Filters. Photogrammetric Engineering & Remote Sensing 65, 453–458.
 Zhang, L., Wu, J., Hao, T., Wang, J., 2006. Automatic lineament extraction from potential-field images using the Radon transform and gradient calculation. Geophysics 71, 31-40.
Advanced Applied Geology
Volume 9, Issue 4
January 2020
Pages 374-391

Files

Share

How to cite

Statistics