Skip to main content

Advertisement

SpringerLink
Log in

Using edge detection techniques and machine learning classifications for accurate lithological discrimination and structure lineaments extraction: a comparative case study from Gattar area, Northern Eastern Desert of Egypt

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Satellite images are used, among other functions, for geologic feature extraction. Scientists have developed advanced, cutting-edge, and reliable data extraction processing procedures, such as machine learning and edge detection methods, as a result of the rapid development in the volume of remote sensing data received from various platforms. The use of the optimal edge detection techniques is crucial for mapping the lineaments because it is thought that hydrothermal alteration may be related to faults and fractures that are pathways for hydrothermal solutions. Efforts were made in this direction by evaluation and comparison of different optimal edge detector algorithms using the first principal component analyses of ASTER imagery on Gattar area in the Northern Eastern Desert of Egypt. The lineaments identified in the Gattar batholith show mainly the NNE–SSW and NE–SW and nearly E–W strikes as major directions and the NW–SE strike as a minor direction where the dominant structures controlling the mineralization are NNE–SSW and NE–SW trending faults. The (LoG) method was found the most accurate and well-matched with the field investigation in the study area. Machine learning methods are robust in processing spectral and ground truth measurements against noise and uncertainties. Four popular and recently established classification machine learning methods are applied and compared in the area for mapping the lithologic units, and their accuracy was evaluated using a confusion matrix; they are maximum likelihood classifier (MLC), Mahalanobis distance classification (MDC), neural net classification (NNC), and support vector machine (SVM). The results showed that both (MLC) and (SVM) were better at discriminating all the lithological units in the area. As a result, it was established in this study that the most appropriate methods specified for automatic lineament extraction and machine learning successfully map geological structures and lithological units in the study area and can be achieved in different geographical locations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available on request from the corresponding author.

References

  • Abdelkader MA, Watanabe Y, Shebl A, El-Dokouny HA, Dawoud M, Csámer Á (2022) Effective delineation of rare metalbearing granites from remote sensing data using machine learning methods: a case study from the Umm Naggat Area, Central Eastern Desert, Egypt. Ore Geol Rev 150:105184. https://doi.org/10.1016/j.oregeorev.2022.105184

  • Abdul-Qadir AM (2014) Supervised classification for lithologic discrimination in Shaikh Ibrahim area, NW Iraq using Landsat images. Arab J Sci Eng 39:437–451

    Article  Google Scholar 

  • Abrams M (2000) The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER): data products for the high spatial resolution imager on NASA’s Terra platform. Int J Remote Sens 21:847–859

    Article  Google Scholar 

  • Abu Zeid M (1995) Relation between surface and subsurface uranium mineralization and structural features, Gebel Gattar, North Eastern Desert. Faculty of Science, Ain Shams University, Cairo, Egypt, Egypt, p 208

    Google Scholar 

  • Abubakar FM (2012) Study of image segmentation by using edge detection techniques. Int J Eng Res Technol (IJERT) 1(9):1–5

    Google Scholar 

  • Alshayef M, Mohammed A, Javed A, Albaroot M (2017) Manual and automatic extraction of lineaments from multispectral image in part of Al-Rawdah, Shabwah, Yemen by using remote sensing and GIS technology. Int j New Technol Res 3:263346

    Google Scholar 

  • Amin NF (2010) Surface and subsurface structural features controlling uranium mineralizations at granitic-hammamat contact. Faculty of Science, Ain Shams University, Egypt, Wadi Belieh, Northern Eastern Desert, Egypt, p 98

    Google Scholar 

  • Ansari MA, Kurchaniya D, Dixit M (2017) A comprehensive analysis of image edge detection techniques. Int J Multimed Ubiquitous Eng 12:1–12

    Article  Google Scholar 

  • Argialas D, Mavrantza O (2004) Comparison of edge detection and Hough transform techniques for the extraction of geologic features. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, p 34

    Google Scholar 

  • Bachri I, Hakdaoui M, Raji M, Teodoro AC, Benbouziane A (2019) Machine learning algorithms for automatic lithological mapping using remote sensing data: a case study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas. Morocco ISPRS Int j Geo-Inf 8:248

    Article  Google Scholar 

  • Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. Proceedings of the fifth annual ACM workshop on Computational Learning Theory, pp 144–152

  • Burgan HI, Aksoy H (2022) Daily flow duration curve model for ungauged intermittent subbasins of gauged rivers. J Hydrol 604:127249

    Article  Google Scholar 

  • Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8:679–698

    Article  Google Scholar 

  • Chakouri M, El Harti A, Lhissou R, El Hachimi J, Jellouli A (2020) Geological and mineralogical mapping in Moroccan central Jebilet using multispectral and hyperspectral satellite data and machine learning. Int J 9:5772–5783

    Google Scholar 

  • Cracknell MJ, Reading AM (2014) Geological mapping using remote sensing data: a comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Comput and Geosci 63:22–33

    Article  Google Scholar 

  • Christie D, Neill SP (2022) Measuring and observing the ocean renewable energy resource. In: Letcher TM (ed) Comprehensive Renewable Energy (2nd ed). Elsevier, Oxford, 8:149–175. https://doi.org/10.1016/B978-0-12-819727-1.00083-2

  • Crosta A, Rabelo A (1993) Assessing Landsat/TM for hydrothermal alteration mapping in Central-Western Brazil. Proceedings of the Ninth Thematic Conference on Geologic Remote Sensing, Pasadena, California, USA. Environmental Research Institute of Michigan, pp 1053–1053

  • Duda K, Daucsavage J, Siemonsma D, Brooks B, Oleson R, Meyer D, Doescher C (2015) Advanced spaceborne thermal emission and reflection radiometer (aster) level 1 precision terrain corrected registered at-sensor radiance product (ast_l1t). US Geological Survey, USA

    Google Scholar 

  • El Rakaiby ML, Shalaby MH (1992) Geology of Gebel Qattar batholith, Central Eastern Desert. Egypt Int J Remote Sens 13:2337–2347

    Article  Google Scholar 

  • El Kholy DM, Khamis HA, El Sundoly HI (2019) Geology and structural relationship between uranium occurrences in the northern part of Gabal Gattar, Northern Eastern Desert Egypt. Nuclear Sci Sci J 8(A):1–18

    Google Scholar 

  • Esmail EM (2002) Subsurface geological study of Wadi Beli uranium occurrence, north Eastern Desert. El Azhar University, Egypt, p 204

    Google Scholar 

  • Fitton NC, Cox SJD (1998) Optimizing the application of Hough transform for automatic feature extraction from geoscientific images. Comput and Geosci 24(10):933–951

    Article  Google Scholar 

  • Gad S, Kusky T (2007) ASTER spectral ratioing for lithological mapping in the Arabian-Nubian shield, the Neoproterozoic Wadi Kid area, Sinai. Egypt Gondwana Res 11:326–335

    Article  Google Scholar 

  • Green AA, Berman M, Switzer P, Craig MD (1988) A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans Geosci Remote Sens 26:65–74

    Article  Google Scholar 

  • Guo Y, Fu Y, Hao F, Zhang X, Wu W, Jin X, Bryant CR, Senthilnath J (2021) Integrated phenology and climate in rice yields prediction using machine learning methods. Ecol Indic 120:106935

    Article  Google Scholar 

  • Guo Y, Chen S, Li X, Cunha M, Jayavelu S, Cammarano D, Fu Y (2022) Machine learning-based approaches for predicting SPAD values of maize using multi-spectral images. Remote Sens 14(6):1337

    Article  Google Scholar 

  • Gupta RP (2017) Remote sensing geology. Springer

    Google Scholar 

  • Hammouda EMA (2003) Comparative mineralogical and geochemical studies of some surface and subsurface granite samples. Faculty of Science, Al-Azhar University, Egypt, Gattar-I uranium occurrence, North Eastern Desert, Egypt, p 212

    Google Scholar 

  • Haridy MH (1995) Physical and mechanical properties of Gabal Gattar granitic pluton and the relation to joint-type U-mineralization. Cairo University, Cairo, Egypt, p 172

    Google Scholar 

  • Haykin S (2004) Neural networks, A comprehensive foundation, 2nd edn. Prentice Hall Upper Saddle River, New Jersey

    Google Scholar 

  • Hung L, Batelaan O, De Smedt F (2005) Lineament extraction and analysis, comparison of LANDSAT ETM and ASTER imagery. Case study: Suoimuoi tropical karst catchment, Vietnam, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology V. International Society for Optics and Photonics, 5983:182–193. https://doi.org/10.1117/12.627699

  • Karnieli A, Meisels A, Fisher L, Arkin Y (1996) Automatic extraction of geological linear features from digital remote sensing data using a Hough Transform. Photogramm Eng Remote Sens 62:525–531

    Google Scholar 

  • Kobler A, Adamic M (2000) Identifying brown bear habitat by a combined GIS and machine learning method. Ecol Modell 135(2–3):291–300

    Article  Google Scholar 

  • Kommey B, Dunyo JK, Tchao ET, Agbemenu AS (2021) A comparative study of common edge detection operators in digital image processing. J Eng Appl Sci 6(4):147–152. https://doi.org/10.46565/jreas.2021.v06i04.001

    Article  Google Scholar 

  • Krause P, Boyle DP, Bäse F (2005) Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5(5):89–97. https://doi.org/10.5194/adgeo-5-89-2005

    Article  Google Scholar 

  • Latifovic R, Pouliot D, Campbell J (2018) Assessment of convolution neural networks for surficial geology mapping in the South Rae geological region, Northwest Territories. Canada Remote Sens 10:307. https://doi.org/10.3390/rs10020307

    Article  Google Scholar 

  • Mahdy NM (2011) Mineralogical studies and mineral chemistry of some radioactive mineralizations in Gabal Gattar area. Ain Shams University, Egypt, Northern Eastern Desert, Egypt, p 186

    Google Scholar 

  • Mahdy NM (2015) A Genetic Model for Molybdenum and Uranium Mineralizations i n Gabal Gattar Granite. Ain Shams University, Northern Eastern Desert, Egypt, p 191

    Google Scholar 

  • Mahdy NM, Shalaby MH, Helmy HM, Osman AF, El Sawey ESH, Zeid EKA (2014) Trace and REE element geochemistry of fluorite and its relation to uranium mineralizations, Gabal Gattar Area, Northern Eastern Desert. Egypt Arab J Geosci 7:2573–2589

    Google Scholar 

  • Mahdy NM, El Kalioubi BA, Wohlgemuth-Ueberwasser CC, Shalaby MH, El-Afandy AH (2015) Petrogenesis of U-and Mo-bearing A2-type granite of the Gattar batholith in the Arabian Nubian Shield, Northeastern Desert, Egypt: evidence for the favorability of host rocks for the origin of associated ore deposits. Ore Geol Rev 71:57–81

    Article  Google Scholar 

  • Maini R, Aggarwal H (2009) Study and comparison of various image edge detection techniques. Int J Image Processing (IJIP) 3:1–11

    Google Scholar 

  • Manap HS, San BT (2022) Data Integration for Lithological Mapping Using Machine Learning Algorithms. Earth Sci Inform 15:1841–1859. https://doi.org/10.1007/s12145-022-00826-3

    Article  Google Scholar 

  • Marghany M, El Mahdi SI, Hashim M (2006) Automatic detection of lineaments from LANDSAT images in United Arab Emirate (UAE). In: International Conference on Space Technology and Geo-Informatics, UTM Malaysia 8:1–13

  • Mark H, Workman J (2018) Chapter 75 - The Statistics of Spectral Searches, Section 13 - Limitations in Analytical Accuracy. In: Chemometrics in Spectroscopy (2nd edn). Academic Press, pp 507–511

  • Marpu PR, Niemeyer I, Nussbaum S, Gloaguen R (2008) A procedure for automatic Object-Based classification.A procedure for automatic object-based classification. In: Blaschke T, Lang S, Hay GJ (eds) Object-Based Image Analysis. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg, pp 169–184

  • Mars JC, Rowan LC (2011) ASTER spectral analysis and lithologic mapping of the Khanneshin carbonatite volcano, Afghanistan. Geosphere 7:276–289

    Article  Google Scholar 

  • Mohamed RM, Kamel AI (2018) Edge detection enhanced technique of satellite images using design digital filter. Nuclear Sci Scientific J 7:205–213

    Article  Google Scholar 

  • Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900. https://doi.org/10.13031/2013.23153

    Article  Google Scholar 

  • Morris K (1991) Using knowledge-base rules to map the trhree dimentional nature of geological features. Photogrammetric Eng and Remote Sens 57:1209–1216

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: Part 1 A Discussion of Principles. J Hydrology 10(3):282–290

    Article  Google Scholar 

  • Ndehedehe CE, Oludiji SM, Asuquo IM (2013) Supervised Learning Methods in the Mapping of Built Up Areas from Landsat-Based Satellite Imagery in Part of Uyo Metropolis. N Y Sci j 6:45

    Google Scholar 

  • Ninomiya Y, Fu B, Cudahy TJ (2005) Detecting lithology with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral thermal infrared “radiance-at-sensor” data. Remote Sens Environ 99:127–139

    Article  Google Scholar 

  • Nossent J, Bauwens W (2012) Application of a normalized Nash–Sutcliffe efficiency to improve the accuracy of the Sobol’ sensitivity analysis of a hydrological model. In: Conference European Geosciences Union General Assembly, Vienna, Austria, Geophysical Research Abstracts, 14:EGU2012–237

  • O’Leary D, Friedman J, Pohn H (1976) Lineament, linear, lineation: some proposed new standards for old terms. Geol Soc Am Bull 87:1463–1469

    Article  Google Scholar 

  • Pham BT, Bui DT, Prakash I, Dholakia MB (2017) Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149:52–63

    Article  Google Scholar 

  • Philip G (1996) Landsat thematic mapper data analysis for the quaternary tectonics in parts of the Doon Valley, NW Himalaya, India. Int J of Remote Sensing 17:143–153

    Article  Google Scholar 

  • Prabhakaran S (2019) Mahalanobis Distance-Understanding the math with examples (python). Machine Learning Plus. https://www.machinelearningplus.com/statistics/mahalanobis-distance/

  • Prewitt JM (1970) Object enhancement and extraction. Picture Processing Psychopictorics 10:15–19

    Google Scholar 

  • Rao MS, Kartheek V, Madhu T (2015) Classification of Satellite Broadcasting Image and Validation Exhausting Geometric Interpretation. Int J Sci Res Dev (IJSRD) 3(10):57–69

  • Ritter A, Muñoz-Carpena R (2013) Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodness-of-fit assessments. J Hydrology 480(1):33–45. https://doi.org/10.1016/j.jhydrol.2012.12.004

    Article  Google Scholar 

  • Roberts L (1965) Machine Perception of three-dimensional solids, in Optical and Electro-Optical information processing (J. Tippett, eds.). MIT Press, pp 159–197

  • Roushdy M (2006) Comparative study of edge detection algorithms applying on the grayscale noisy image using morphological filter. GVIP Journal 6:17–23

    Google Scholar 

  • Roz ME (1994) Geology and uranium mineralization of Gabal Gattar area, north Eastern Desert. Al Azhar University, Cairo, Egypt, Egypt, p 175

    Google Scholar 

  • Sabins FF (2007) Remote sensing: principles and applications. Waveland Press

    Google Scholar 

  • Salman AB, El Kholy DM, El Zalaky MA (2005) Relation between granite plutonism, faulting and uranium mineralization in the northern part of Gabal Qattar area, Nortern Eastern Desert. Egypt Al-Azhar Bull Sci 16:23–35

    Google Scholar 

  • Sander P (2007) Lineaments in groundwater exploration: a review of applications and limitations. Hydrogeol J 15:71–74

    Article  Google Scholar 

  • Sang X, Xue L, Ran X, Li X, Liu J, Liu Z (2020) Intelligent high-resolution geological mapping based on SLIC-CNN. ISPRS Int j Geo-Inf 9:99. https://doi.org/10.3390/ijgi9020099

    Article  Google Scholar 

  • Savant S (2014) A review on edge detection techniques for image segmentation. Int j Comput Sci Inf Technol Res 5:5898–5900

    Google Scholar 

  • Sefercik U, Gulegen O (2004) Edge detection in geologic formation, Extraction: close range and remote sensing case studies, In Conference: XX. ISPRS Kongresi, “Geo-imagery Bridging the Continents”, Istanbul, Turkey

  • Shalaby MH (1996) Structural controls of uranium mineralization at Gabal Gattar, north Eastern Desert. Egypt, Egyptian Academy of Sciences, pp 521–536

    Google Scholar 

  • Shalaby MH, Abu Zeid EK, Mahdy NM (2015) On the petrogenesis and evolution of U-rich granite: insights from mineral chemistry studies of Gattar granite, North Eastern Desert. Egypt Arab J Geosci 8:3565–3585

    Article  Google Scholar 

  • Shawky M, Zalaky MAE, Elarif T, Elsaid M (2019a) Comparative study of atmospheric correction methods of ASTER data to enhance the delineation of uranium mineralized zones. Int j Intell Computing Inf Sci 19:48–65

    Google Scholar 

  • Shawky MM, El-Arafy RA, El Zalaky MA, Elarif T (2019b) Validating (MNF) transform to determine the least inherent dimensionality of ASTER image data of some uranium localities at Central Eastern Desert. Egypt J African Earth Sci 149:441–450

    Article  Google Scholar 

  • Shebl A, Kusky T, Csámer Á (2022) Advanced land imager superiority in lithological classification utilizing machine learning algorithms. Arab J Geosci 15:1–13

    Article  Google Scholar 

  • Sheikhrahimi A, Pour AB, Pradhan B, Zoheir B (2019) Mapping hydrothermal alteration zones and lineaments associated with orogenic gold mineralization using ASTER data: a case study from the Sanandaj-Sirjan Zone. Iran Adv Space Res 63:3315–3332

    Article  Google Scholar 

  • Shirmard H, Farahbakhsh E, Pour AB, Muslim AM, Müller RD, Chandra R (2020) Integration of selective dimensionality reduction techniques for mineral exploration using ASTER satellite data. Remote Sensing 12:1261

    Article  Google Scholar 

  • Shirmard H, Farahbakhsh E, Müller RD, Chandra R (2022) A review of machine learning in processing remote sensing data for mineral exploration. Remote Sens Environ 268(26):112750. https://doi.org/10.1016/j.rse.2021.112750

    Article  Google Scholar 

  • Singla P, Duhan M, Saroha S (2022) Different normalization techniques as data preprocessing for one step ahead forecasting of solar global horizontal irradiance. In Artificial Intelligence for Renewable Energy Systems. Elsevier, pp 209–230. https://doi.org/10.1016/B978-0-323-90396-7.00004-3

  • Sobel I (1990) An isotropic 3× 3 image gradient operator, machine vision for three-dimensional scenes. Freeman, H., Academic Press, NY, pp 376–379

  • Solomon C, Breckon T (2011) Fundamentals of Digital Image Processing: A practical approach with examples in Matlab. John Wiley & Sons

    Google Scholar 

  • Torre V, Poggio T (1986) On edge detection. IEEE Trans Pattern Anal Mach Intell 8:187–163

    Google Scholar 

  • Tözün KA, Özyavaş A (2022) Automatic detection of geological lineaments in central Turkey based on test image analysis using satellite data. Adv Space Res 69:3283–3300

    Article  Google Scholar 

  • Umbaugh SE (2010) Digital image processing and analysis: human and computer vision applications with CVIPtools (2nd ed). CRC press, Taylor & Frances Group, Boca Raton, FL, pp 955

  • Vaz DA, Di Achille G, Barata MT, Alves EI (2008) Manual and Automatic Lineament Mapping: Comparing results. Lunar and Planetary Science XXXIX

    Google Scholar 

  • Zaki MA (2002) Interplay of plutonism, faulting and mineralization, northern Gabal Gattar peripheral zone, north Eastern Desert. Faculty of Science, Zagazig University, Banha Branch, Egypt, Egypt, p 178

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank Prof. Hamid Mira chairman of Nuclear Materials Authority for his help in discussions and with encourages along the progression of the work. The author also would like to acknowledge NASA and USGS for remote sensing data (ASTER) used in this study.

Author information

Authors and Affiliations

  1. Nuclear Materials Authority, El-Maadi, P.O. Box 530, Cairo, Egypt

    Reda A. El-Arafy, Mahinaz M. Shawky & Nasser M. Mahdy

  2. Faculty of Computers and Information, Zagazig University, Zagazig, Egypt

    Ahmed M. Abdelmouty

Authors
  1. Reda A. El-Arafy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahinaz M. Shawky
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nasser M. Mahdy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed M. Abdelmouty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Reda A. El-Arafy or Mahinaz M. Shawky.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Biswajeet Pradhan

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El-Arafy, R.A., Shawky, M.M., Mahdy, N.M. et al. Using edge detection techniques and machine learning classifications for accurate lithological discrimination and structure lineaments extraction: a comparative case study from Gattar area, Northern Eastern Desert of Egypt. Arab J Geosci 16, 619 (2023). https://doi.org/10.1007/s12517-023-11732-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-023-11732-3

Keywords

Search

Navigation