Automatic Identification of Rock-Forming Minerals in Granite Using Laboratory Scale Hyperspectral Reflectance Imaging and Artificial Neural Networks
Source abstract
Abstract
Laser cleaning of stones is a well-established technique in conservation of Cultural Heritage. In case of polymineralic granular rocks like granite, the tuning of the laser parameters for safe cleaning becomes a demanding task due to the differential response of the constituent minerals to laser irradiation. In this sense, a compromise solution must be reached between removing unwanted layers and safeguarding the original characteristics of the stone substrate. Consequently, the need of non-invasive, rapid and accurate methods for in situ identification of the minerals in the stone surface is highlighted. The aim of this paper is to demonstrate the ability of a laboratory scale hyperspectral reflectance imaging system, in combination with artificial neural networks for in situ, accurate identification of constituent minerals of a hercynian granite; with the ultimate goal of achieving automatic adjustment of laser irradiation parameters in cleaning processes. The effectiveness of a neural network with the structure of a three-layer perceptron was evaluated by comparing calculated results with modal analysis composition obtained by petrographic microscope and a high degree of accordance, above 95%, was achieved.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Geochemistry and Geologic Mapping · Remote-Sensing Image Classification · Spectroscopy and Chemometric Analyses
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
Show source-provided references
1. AENOR.: UNE-EN 12407:2007. Natural Stone Test Methods - Petrographic Examination. AENOR. http://www.en.aenor.es/aenor/normas/normas/fichanorma.asp?tipo=N&codigo=N0040172#.WPc0QqLtaUk (2007)
2. Baissa, Rachid, Labbassi, Kamal, Launeau, Patrick, Gaudin, Anne, Ouajhain, Brahim: Using HySpex SWIR-320m hyperspectral data for the identification and mapping of minerals in hand specimens of carbonate rocks from the Ankloute Formation (Agadir Basin, Western Morocco). J. Afr. Earth Sci. 61(1), 1–9 (2011). doi: 10.1016/j.jafrearsci.2011.04.00310.1016/j.jafrearsci.2011.04.003 ↗
3. Baykan, Nurdan Akhan, Yılmaz, Nihat: Mineral identification using color spaces and artificial neural networks. Comput. Geosci. 36(1), 91–97 (2010). doi: 10.1016/j.cageo.2009.04.00910.1016/j.cageo.2009.04.009 ↗
4. Beale, M.H., Hagan, M.T., Demuth, H.B.: Neural Network Toolbox $$\rm TM$$ TM User’s Guide, The MathWorks, Inc. https://www.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf (2017). Accessed 21 June 2017
5. Dai, Qiong, Sun, Da-Wen, Xiong, Zhenjie, Cheng, Jun-Hu, Zeng, Xin-An: Recent advances in data mining techniques and their applications in hyperspectral image processing for the food industry. Compr. Rev. Food Sci. Food Saf. 13(5), 891–905 (2014). doi: 10.1111/1541-4337.1208810.1111/1541-4337.12088 ↗
6. Delgado Rodrigues, J., Costa, D., Mascalchi, M., Osticioli, I., Siano, S.: Laser ablation of iron-rich black films from exposed granite surfaces. Appl. Phys. A 117(1), 365–370 (2014). doi: 10.1007/s00339-014-8470-810.1007/s00339-014-8470-8 ↗
7. Edelman, G.J., Gaston, E., van Leeuwen, T.G., Cullen, P.J., Aalders, M.C.G.: Hyperspectral imaging for non-contact analysis of forensic traces. Forensic Sci. Int. 223(1), 28–39 (2012). doi: 10.1016/j.forsciint.2012.09.01210.1016/j.forsciint.2012.09.012 ↗
8. Eismann, M.T.: Hyperspectral Remote Sensing. SPIE, Bellingham (2012)10.1117/3.899758 ↗
9. Esbert, R.M., Grossi, C.M., Rojo, A., Alonso, F.J., Montoto, M., Ordaz, J., de Andrés, M.P., et al.: application limits of Q-switched Nd:YAG laser irradiation for stone cleaning based on colour measurements. J. Cult. Herit. 4(1), 50–55 (2003)10.1016/S1296-2074(02)01227-X ↗
10. Fiorucci, M.P., López, A. J., Ramil, A., Pozo, S., Rivas, T.: Monitoring the Laser Cleaning of Granites by Means of Multispectral Image Analysis. In: Manuel Filipe P.C. Martins Costa and Rogério Nunes Nogueira (eds.), 928610. doi: 10.1117/12.2063777 (2014)10.1117/12.2063777 ↗
11. Fiorucci, M.P., López, A.J., Ramil, A., Pozo, S., Rivas, T.: Optimization of Graffiti removal on natural stone by means of high repetition rate UV laser. Appl. Surf. Sci. 278, 268–272 (2013). doi: 10.1016/j.apsusc.2012.10.09210.1016/j.apsusc.2012.10.092 ↗
12. Fotakis, C.: Lasers in the Preservation of Cultural Heritage?: Principles and Applications. Taylor & Francis, Milton Park (2007)
13. Goetz, Alexander F.H.: Three decades of hyperspectral remote sensing of the earth: a personal view. Remote Sens. Environ. 113, S5–16 (2009). doi: 10.1016/j.rse.2007.12.01410.1016/j.rse.2007.12.014 ↗
14. Gowen, A.A., Feng, Y., Gaston, E., Valdramidis, V.: Recent applications of hyperspectral imaging in microbiology. Talanta 137, 43–54 (2015). doi: 10.1016/j.talanta.2015.01.01210.1016/j.talanta.2015.01.012 ↗
15. Grossi, Carlota M., Alonso, Franeisco Javier, Esbert, Rosa M., Rojo, Araceli: Effect of laser cleaning on granite color. Color Res. Appl. 32(2), 152–159 (2007). doi: 10.1002/col.2029910.1002/col.20299 ↗
16. Haykin, S.S., Haykin, S.S.: Neural Networks and Learning Machines. Prentice Hall/Pearson, Upper Saddle River (2009)
17. Iglesias, C., Torres, J.M., Nieto, P.G., Fernández, J.A., Muñiz, C.D., Piñeiro, J.I., Taboada, J.: Turbidity prediction in a River Basin by using artificial neural networks: a case study in Northern Spain. Water Resour. Manag. 28(2), 319–331 (2014). doi: 10.1007/s11269-013-0487-910.1007/s11269-013-0487-9 ↗
18. Izadi, H., Sadri, J., Bayati, M.: An intelligent system for mineral identification in thin sections based on a cascade approach. Comput. Geosci. 99, 37–49 (2017). doi: 10.1016/j.cageo.2016.10.01010.1016/j.cageo.2016.10.010 ↗
19. Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.: Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8–17 (2015). doi: 10.1016/j.csbj.2014.11.00510.1016/j.csbj.2014.11.005 ↗
20. Kruse, F.A.: Mapping surface mineralogy using imaging spectrometry. Geomorphology 137(1), 41–56 (2012). doi: 10.1016/j.geomorph.2010.09.03210.1016/j.geomorph.2010.09.032 ↗
21. Liang, H.: Advances in multispectral and hyperspectral imaging for archaeology and art conservation. Appl. Phys. A 106(2), 309–323 (2012)10.1007/s00339-011-6689-1 ↗
22. López, A.J., Lamas, J., Ramil, A., Yáñez, A., Rivas, T., Taboada, J.: Optimization of Laser Cleaning Parameters for the Removal of Biological Black Crusts in Granites. In: Lasers in the Conservation of Artworks VIII—Proceedings of the International Conference on Lasers in the Conservation of Artworks VIII, LACONA VIII. http://www.scopus.com/inward/record.url?eid=2-s2.0-84860807113&partnerID=MN8TOARS (2011)
23. López, A.J., Nicolás, G., Mateo, M.P., Ramil, A., Piñón, V., Yáñez, A.: LIPS and linear correlation analysis applied to the classification of Roman Pottery Terra Sigillata. Mater. Sci. Process. Appl. Phys. A 83(4), 695–698 (2006). doi: 10.1007/s00339-006-3556-610.1007/s00339-006-3556-6 ↗
24. Lu, G., Fei, B.: Medical hyperspectral imaging: a review. J. Biomed. Opt. 19(1), 010901 (2014). doi: 10.1117/1.JBO.19.1.01090110.1117/1.JBO.19.1.010901 ↗
25. Van der Meer, F.D., Van der Werff, H.M., van Ruitenbeek, F.J., Hecker, C.A., Bakker, W.H., Noomen, M.F., van der Meijde, M., Carranza, E.J.M., de Smeth, J.B., Woldai, T.: Multi- and hyperspectral geologic remote sensing: a review. Int. J. Appl. Earth Obs. Geoinf. 14(1), 112–128 (2012). doi: 10.1016/j.jag.2011.08.00210.1016/j.jag.2011.08.002 ↗
26. Pozo-Antonio, J.S., Fiorucci, M.P., Ramil, A., Rivas, T., López, A.J.: Hyperspectral imaging as a non destructive technique to control the laser cleaning of graffiti on granite. J. Nondestr. Eval. 35(3), 44 (2016). doi: 10.1007/s10921-016-0361-910.1007/s10921-016-0361-9 ↗
27. Pozo-Antonio, J.S., Fiorucci, M.P., Rivas, T., López, A.J., Ramil, A., Barral, D.: Suitability of hyperspectral imaging technique to evaluate the effectiveness of the cleaning of a crustose lichen developed on granite. Appl. Phys. A 122(2), 100 (2016). doi: 10.1007/s00339-016-9634-510.1007/s00339-016-9634-5 ↗
28. Pozo-Antonio, J.S., Ramil, A., Fiorucci, M.P., López, A.J., Rivas, T.: The use of hyperspectral imaging technique to detect the most suitable graffiti-cleaning procedure. Color Res. Appl. 41(3), 308–312 (2016). doi: 10.1002/col.2203210.1002/col.22032 ↗
29. Pozo-Antonio, J.S., Fiorucci, M.P., Ramil, A., López, A.J., Rivas, T.: Evaluation of the effectiveness of laser crust removal on granites by means of hyperspectral imaging techniques. Appl. Surf. Sci. 347, 832–838 (2015). doi: 10.1016/j.apsusc.2015.04.18210.1016/j.apsusc.2015.04.182 ↗
30. Ramil, A., López, A.J., Yáñez, A.: Application of artificial neural networks for the rapid classification of archaeological ceramics by means of laser induced breakdown spectroscopy (LIBS). Appl. Phys. A 92(1), 197–202 (2008). doi: 10.1007/s00339-008-4481-710.1007/s00339-008-4481-7 ↗
31. Riaza, A., Strobl, P., Beisl, U., Hausold, A.: Spectral mapping of rock weathering degrees on granite using hyperspectral DAIS 7915 spectrometer data. Int. J. Appl. Earth Obs. Geoinf. 3(4), 345–354 (2001). doi: 10.1016/S0303-2434(01)85042-X10.1016/S0303-2434(01)85042-X ↗
32. Rivas, T., Lopez, A.J., Ramil, A., Pozo, S., Fiorucci, M.P., De Silanes, M.E.L., García, A., De Aldana, J.R.V., Romero, C., Moreno, P.: Comparative study of ornamental granite cleaning using femtosecond and nanosecond pulsed lasers. Appl. Surf. Sci. 278, 226–233 (2013). doi: 10.1016/j.apsusc.2012.12.03810.1016/j.apsusc.2012.12.038 ↗
33. Smith, John V., Beermann, Eberhard: Image analysis of plagioclase crystals in rock thin sections using grey level homogeneity recognition of discrete areas. Comput. Geosci. 33(3), 335–356 (2007). doi: 10.1016/j.cageo.2005.11.01010.1016/j.cageo.2005.11.010 ↗
34. Thompson, Sean, Fueten, Frank, Bockus, David: Mineral identification using artificial neural networks and the rotating polarizer stage. Comput. Geosci. 27(9), 1081–1089 (2001). doi: 10.1016/S0098-3004(00)00153-910.1016/S0098-3004(00)00153-9 ↗
35. Zaini, N., van der Meer, F., van der Werff, H.: Determination of carbonate rock chemistry using laboratory-based hyperspectral imagery. Remote Sens. 6(5), 4149–4172 (2014). doi: 10.3390/rs605414910.3390/rs6054149 ↗