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Hyperspectral Technology for Oil Spills Detection by Using Artificial Neural Network Classifier

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18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (SOCO 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 749))

Abstract

Hyperspectral sensors in marine pollution detection, such as oil spills from maritime traffic, represent an innovative and smart solution that addresses rapid detection and supports early oil spill cleanup operations. A spectroradiometer has been used to collect the spectral signatures of polluted water, including different thicknesses of oil films to achieve concentration determination. The spectral resolution provided by the spectroradiometer (1 nm), which works from the visible-near infrared (VNIR, 350–1000 nm) to the short-wavelength infrared (1000 - 2500 nm), provides a continuous spectral signature yielding rich and complete spectral information about the material. The high volume of data generated by hyperspectral technology requires a feature selection procedure for better management and optimization of computing resources. In this study, Principal Component Analysis (PCA) has been the statistical method used for dimensional reduction. The results are very promising, since PCA succeeded in defining the original dataset in a three-dimensional space, maintaining 80% of its variance. Likewise, it has also been possible to establish that the wavelengths ranged between 449 nm and 549 nm, and the infrared from 750 nm ahead, are the most influential wavelengths in the characterization of the data. Classification task has been performed with ANNs, whose hyperparameters have been determined with Bayesian Optimisation. A shallow ANN with 10 neurons has been selected with an accuracy of 100% in the detection of polluted water and 96,7% accuracy in the determination of the classes provided, with a failure rate of 16.7% for the discrimination of oil concentration alone.

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References

  1. European Commission, Press release. Ocean biodiversity: global agreement on protection and sustainable use of resources and biodiversity in high seas, March 2023. https://ec.europa.eu/commission/presscorner/detail/es/ip_23_1382. Accessed 06 Mar 2023

  2. Yang, J., et al.: Decision fusion of deep learning and shallow learning for marine oil spill detection. Remote Sens. (Basel) 14(3), 666 (2022). https://doi.org/10.3390/rs14030666

    Article  Google Scholar 

  3. EMSA and EEA, “EUROPEAN Maritime Transport Environmental Report 2021,” Publications Office of the European Union (2021)

    Google Scholar 

  4. Aguilera, F., Méndez, J., Pásaroa, E., Laffona, B.: Review on the effects of exposure to spilled oils on human health. J. Appl. Toxicol. 30(4), 291–301 (2010). https://doi.org/10.1002/jat.1521

    Article  Google Scholar 

  5. Ugwu, C.F., Ogba, K.T., Ugwu, C.S.: Ecological and economic costs of oil spills in Niger delta, Nigeria. In: Economic Effects of Natural Disasters: Theoretical Foundations, Methods, and Tools, pp. 439–455. Elsevier (2020). https://doi.org/10.1016/B978-0-12-817465-4.00026-1

  6. Yaghmour, F., et al.: Oil spill causes mass mortality of sea snakes in the Gulf of Oman. Sci. Total Environ. 825, 154072 (2022). https://doi.org/10.1016/j.scitotenv.2022.154072

    Article  Google Scholar 

  7. Cirer-Costa, J.C.: Tourism and its hypersensitivity to oil spills. Mar. Pollut. Bull. 91(1), 65–72 (2015). https://doi.org/10.1016/j.marpolbul.2014.12.027

    Article  Google Scholar 

  8. Leifer, I., et al.: State of the art satellite and airborne marine oil spill remote sensing: application to the BP Deepwater horizon oil spill. Remote Sens. Environ. 124, 185–209 (2012). https://doi.org/10.1016/j.rse.2012.03.024

    Article  Google Scholar 

  9. Deepthi, Thomas, T.: Spectral similarity algorithm-based image classification for oil spill mapping of hyperspectral datasets. J. Spectral Imag. 9, 1–17 (2020). https://doi.org/10.1255/jsi.2020.a14

  10. El-Rahman, S.A., Zolait, A.H.S.: Hyperspectral image analysis for oil spill detection: a comparative study. Int. J. Comput. Sci. Math. 9(2), 103–121 (2018). https://doi.org/10.1504/IJCSM.2018.091744

    Article  Google Scholar 

  11. Wettle, M., Daniel, P.J., Logan, G.A., Thankappan, M.: Assessing the effect of hydrocarbon oil type and thickness on a remote sensing signal: a sensitivity study based on the optical properties of two different oil types and the HYMAP andQuickbird sensors. Remote Sens. Environ. 113(9), 2000–2010 (2009). https://doi.org/10.1016/j.rse.2009.05.010

    Article  Google Scholar 

  12. Jiang, Z., Ma, Y., Yang, J.: Inversion of the thickness of crude oil film based on an OG_CNN model. J. Mar. Sci. Eng. 8(9), 1–21 (2020). https://doi.org/10.3390/jmse8090653

    Article  Google Scholar 

  13. Lu, Y., Tian, Q., Wang, X., Zheng, G., Li, X.: Determining oil slick thickness using hyperspectral remote sensing in the Bohai Sea of China. Int. J. Digit. Earth 6(1), 76–93 (2013). https://doi.org/10.1080/17538947.2012.695404

    Article  Google Scholar 

  14. Lv, W., Wang, X.: Overview of Hyperspectral Image Classification. J. Sens. 2020 (2020). https://doi.org/10.1155/2020/4817234

  15. Wambugu, N., et al.: Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: a review. Int. J. Appl. Earth Obs. Geoinf. 105, 102603 (2021). https://doi.org/10.1016/j.jag.2021.102603

    Article  Google Scholar 

  16. Qu, S., Li, X., Gan, Z.: A review of hyperspectral image classification based on joint spatial-spectral features. In: Journal of Physics: Conference Series. IOP Publishing Ltd. (2022).https://doi.org/10.1088/1742-6596/2203/1/012040

  17. Yang, C., Lan, H., Gao, F., Gao, F.: Review of deep learning for photoacoustic imaging. Photoacoustics 21, 100215 (2021). https://doi.org/10.1016/j.pacs.2020.100215

    Article  Google Scholar 

  18. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  19. Gakhar, S., Tiwari, K.C.: Spectral – spatial urban target detection for hyperspectral remote sensing data using artificial neural network. Egypt. J. Remote Sens. Space Sci. 24(2), 173–180 (2021). https://doi.org/10.1016/j.ejrs.2021.01.002

    Article  Google Scholar 

  20. Klem, K., et al.: Improving nitrogen status estimation in malting barley based on hyperspectral reflectance and artificial neural networks. Agronomy 11(12), 2592 (2021). https://doi.org/10.3390/AGRONOMY11122592

    Article  Google Scholar 

  21. Martín, M.L., et al.: Prediction of CO maximum ground level concentrations in the Bay of Algeciras, Spain using artificial neural networks. Chemosphere 70(7), 1190–1195 (2008). https://doi.org/10.1016/j.chemosphere.2007.08.039

    Article  Google Scholar 

  22. Muñoz, E., Martín, M.L., Turias, I.J., Jimenez-Come, M.J., Trujillo, F.J.: Prediction of PM10 and SO2 exceedances to control air pollution in the Bay of Algeciras, Spain. Stoch. Env. Res. Risk Assess. 28(6), 1409–1420 (2014). https://doi.org/10.1007/s00477-013-0827-6

    Article  Google Scholar 

  23. Kaymak, S., Helwan, A., Uzun, D.: Breast cancer image classification using artificial neural networks. Procedia Comput. Sci. 120, 126–131 (2017). https://doi.org/10.1016/j.procs.2017.11.219

    Article  Google Scholar 

  24. Miguel, P., Paulo, J.: Classification of electroencephalogram signals using artificial neural networks. In: 2010 3rd International Conference on Biomedical Engineering and Infomatics, Yantai, China, pp. 808–812 (2010). https://doi.org/10.1109/BMEI.2010.5639941

  25. Cao, S., Zhou, S., Liu, J., Liu, X., Zhou, Y.: Wood classification study based on thermal physical parameters with intelligent method of artificial neural networks. BioResources 17(1), 1187–1204 (2022)

    Article  Google Scholar 

  26. Farrugia, J., Griffin, S., Valdramidis, V.P., Camilleri, K., Falzon, O.: Principal component analysis of hyperspectral data for early detection of mould in cheeselets. Curr. Res. Food Sci. 4, 18–27 (2021). https://doi.org/10.1016/j.crfs.2020.12.003

    Article  Google Scholar 

  27. Shan, J., Zhao, J., Liu, L., Zhang, Y., Wang, X., Wu, F.: A novel way to rapidly monitor microplastics in soil by hyperspectral imaging technology and chemometrics. Environ. Pollut. 238, 121–129 (2018). https://doi.org/10.1016/j.envpol.2018.03.026

    Article  Google Scholar 

  28. Tsai, C.L., et al.: Hyperspectral imaging combined with artificial intelligence in the early detection of esophageal cancer. Cancers (Basel) 13(18), 4593 (2021). https://doi.org/10.3390/cancers13184593

    Article  Google Scholar 

  29. Tamilarasi, R., Prabu, S.: Automated building and road classifications from hyperspectral imagery through a fully convolutional network and support vector machine. J. Supercomput. 77(11), 13243–13261 (2021). https://doi.org/10.1007/s11227-021-03954-7

    Article  Google Scholar 

  30. ASDInc. PANalytical Company, FieldSpec ® 4 User Manual (2016)

    Google Scholar 

  31. Goetsz, A.F.H.: Making accurate field spectral reflectance measurements-LR (2012)

    Google Scholar 

  32. Pearson, K.: LIII. On lines and planes of closest fit to systems of points in space. London, Edinb., Dublin Philos. Mag. J. Sci. 2(11), 559–572 (1901)

    Article  Google Scholar 

  33. Hotelling, H.: Analysis of a complex statistical variables into principal components 8. Determination of principal components for individuals. J. Educ. Psychol. 24, 498–520 (1933)

    Article  Google Scholar 

  34. Jolliffe, I.T.: Principal Component Analysis. Springer-Verlag, Cham (1986)

    Book  Google Scholar 

  35. Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A., Arshad, H.: State-of-the-art in artificial neural network applications: a survey. Heliyon 4, 938 (2018). https://doi.org/10.1016/j.heliyon.2018

    Article  Google Scholar 

  36. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. Nautre 323, 533–536 (1986). https://doi.org/10.1038/323533a0

    Article  Google Scholar 

  37. Mockus, J.: On the Bayes methods for seeking the extremal point. IFAC Proc. Volumes 8(1), 428–431 (1975). https://doi.org/10.1016/S1474-6670(17)67769-3

    Article  Google Scholar 

  38. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010). http://www.iro.umontreal

  39. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)

    Google Scholar 

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Acknowledgments

This work is part of the research project EQC2028–004520-P ‘Smart Cities LAB’, FCTA2020–03 ‘Automatic identification of marine spills by using machine learning and hyperspectral technology on UVAs’ and “Control hiperespectral de vertidos de hidrocarburos en aguas marinas y fluviales con machine learning” supported by CEI·MAR. This research is also supported by ‘Plan Propio – UCA 2022–2023’.

Author information

Authors and Affiliations

  1. Department of Industrial and Civil Engineering, School of Engineering, University of Cádiz, Ramón Puyol Av., 11202, Algeciras, Spain

    María Gema Carrasco-García & Juan Jesús Ruiz-Aguilar

  2. Department of Computer Science Engineering, School of Engineering, University of Cádiz, Ramón Puyol Av., 11202, Algeciras, Spain

    María Inmaculada Rodríguez-García, Javier González-Enrique & Ignacio José Turias-Domínguez

  3. Department of Thermal Machines and Motors, School of Engineering, University of Cádiz, Ramón Puyol Av., 11202, Algeciras, Spain

    Paloma Rocío Cubillas-Fernández

Authors
  1. María Gema Carrasco-García
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  3. Javier González-Enrique
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  5. Juan Jesús Ruiz-Aguilar
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  6. Ignacio José Turias-Domínguez
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Correspondence to María Gema Carrasco-García .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. School of Industrial, Computer, University of Leon, León, Spain

    Hilde Pérez García

  3. Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Francisco Javier Martínez de Pisón

  4. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. Applied Computational Intelligence, University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José Luis Calvo Rolle

  8. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  9. Faculty of Science, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Carrasco-García, M.G., Rodríguez-García, M.I., González-Enrique, J., Cubillas-Fernández, P.R., Ruiz-Aguilar, J.J., Turias-Domínguez, I.J. (2023). Hyperspectral Technology for Oil Spills Detection by Using Artificial Neural Network Classifier. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_8

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