Oil Spills Detection from SAR Images Using Wavelets

Year 2018, Volume: 4 Issue: 1, 73 - 80, 12.11.2018

İgor Vujovıć İvica Kuzmanıć

Abstract

Oil spills detection is an actual environmental problem. Oil spills can occur during ships’ oil and/or fuel leakage or in great catastrophes. Small leaks are hardly detectable. Early detection and monitoring of greater spills can be useful in damage suppression and control. A new oil spill detection algorithm is presented in the paper. The algorithm is based on wavelet analysis of the radar image and the data fusion of VTS data, which should correlate to the image processing results to obtain the validated detection. The proposed algorithm exploits both the approximation and the details of the wavelet decomposition.

Keywords

Image processing, Ecology, Oil spill, Data fusion, VTS

References

• Akkartal, A., Sunar, F., (2008). The Usage of Radar Images in Oil Spill Detection. The Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XXXVII/Part B8: 271-276.
• Likoka, A.A., Likoka, A.P., Karathanassi, V., (2015). Water Observation with Radar of Synthetic Opening Images. http://balwois.com/balwois/administration/full_paper /ffp-511.pdf.
• Brekke, C., Solberg, A. (2005). Feature Extraction for Oil Spill Detection Based on SAR Images. Lecture notes in computer science, Springer, vol. 3540, pp. 75–84, Berlin.
• Salberg, A.B., Rudjord, O., Solberg, A.H.S., (2014). Oil Spill Detection in Hybrid Polarimetric SAR Images. IEEE T. Geosci. Remote. 52(10): 6521-6533.
• Santillan, J.R., Paringit, E.C., 2011. Oil Spill Detection in Envisat ASAR Images Using Radar Backscatter Thresholding and Logistic Regression Analysis. 32nd Asian Conference on Remote Sensing (ACRS 2011), 3-7 October 2011, Vol. 1, pp. 473-480, Tapei, Taiwan.
• ElZaart, A. Ghosn, A.A., 2013. SAR images thresholding for oil spill detection. Proc. Saudi Int. Electronics, Communications and Photonics Conference (SIECPC), 27-30 April 2013, pp. 1-5. Riyadh, Saudi Arabia.
• Ramakrishnan, R., Majumdar, T.J., (2013). Classification of oil spill in the Krishna-Godavari offshore using ERS-1 SAR images with a fuzzy logic approach. Indian J. Mar. Sci. 42(4): 431-436.
• Fana, J., Zhangb, F., Zhaob, D., Wanga, J., (2015). Oil Spill Monitoring Based on SAR Remote Sensing Imagery. Aqu. Proc. 2015(3): 112–118.
• Kwon, T.J. & Li, J. (2012). Mapping Marine Oil Spills from Space. In: “Advances in Mapping from Remote Sensor Imagery”, CRC Press, pp. 361–386, London.
• Marghany, M., (2001). RADARSAT Automatic Algorithms for Detecting Coastal Oil Spill Polution. Int. J. Appl. Earth Obs. 3(2): 191-196.
• Karantzalos, K., Argialas, D., (2008). Automatic Detection and Tracking of Oil Spills in SAR Imagery with Level Set Segmentation. Int. J. Remote Sens. 29 (21): 6281-6296.
• Arvelyna, Y., Oshima, M., Kristijono, A., Gunawan, I., 2001. Auto Segmentation of Oil Slick in RADARSAT SAR Image Data around Rupat Island, Malacca Strait. 22nd Asian Conference on Remote Sensing, 5-9 November 2001, pp. 1032-1036, Singapore.
• Keramitsoglou, V., Cartalis, C., Kiranoudis, C., 2002. An integrated fuzzy classification system for automatic oil spill detection using SAR images. Proc. of SPIE, Vol. 4880, pp. 131–140.
• Saleh, N.M. (2004). Automated oil spill detection with ship borne radar. MSc Thesis, International Institute for Geo-information Science and Earth Observation, Enschede, Netherlands.
• Amirmazlaghani, M., Amindavar, H., Moghaddamjoo, A., (2009). Speckle Suppression in SAR Images Using the 2-D GARCH Model. IEEE T. Image. Process. 18(2): 250-259.
• Solberg, A.S., Brekke, C., Solberg, R., Ove Husoy, P., 2004. Algorithms for oil spill detection in Radarsat and ENVISAT SAR images. Proc. 2004 IEEE International Geoscience and Remote Sensing Symposium IGARSS '04, 20-24 September 2004, Vol. 7, pp. 4909 – 4912, Anchorage, AK, USA.
• Gasull, A., Fábregas, X., Jiménez, J., Marqués, F., Moreno, V., Herrero, M.A., (2002). Oil Spills Detection in SAR Images Using Mathematical Morphology, Proc. of the 11th European Signal Processing Conference EUSIPCO'2002, September 2002, , vol. I, pp. 25-28, Toulouse, France.
• Lia, Y., Zhangab, Y., (2014). Synthetic aperture radar oil spills detection based on morphological characteristics. Geo-spatial Inf. Sci. 17(1): 8-16.
• Vyas, K., Patel, U., Shah, P., Zaveri, T., (2015). Enhancing the Capabilities of ImageJ by Incorporating Feature Extraction from SAR Images for Oil Spill Detection. Int. J. Adv. Research Eng. Technol. (IJARET) 6(4): 41-50.
• Keramitsoglou, I., Cartalis, C., Kiranoudis, C.T., (2006). Automatic identification of oil spills on satellite images. Environ. Modell. Softw. 2006(21): 640–652.
• Shu, Y., Li, J., Yousif, H., Gomes, G., (2010). Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring. Remote Sens. Environ. 114: 2026–2035.
• Bhogle, P.M., Patil, S., (2012). Oil Spill Detection in SAR Images Using Texture Entropy Algorithm and Mahalanobis Classifier. Int. J. Eng. Sci. Technol. (IJEST) 2012(4): 4823-4826.
• Marghany, M., Hashim, M., (2011). Comparison between Mahalanobis classification and neural network for oil spill detection using RADARSAT-1 SAR data. Int. J. Phys. Sci. 6(3): 566-576.
• Dongmei, S., Bin, L., Shouchang, C., Yi, M., Yajie, Z., Chen, S., Jianyong, C., (2015). Classification of the Different Thickness of the Oil Film based on Wavelet Transform Spectrum Information. Aquat. Pr. 3: 133–143.
• Longman, F.S., Mihaylova, L.S., Coca, D., 2017. Oil Spill Segmentation in Fused Synthetic Aperture Radar Images. 4th International Conference on Control Engineering & Information Technology (CEIT), 16-18 December 2016, Hammamet, Tunisia.
• Osman, A.B., Mark O.V., Fakhruldin,M.H., Faye, I., (2017). Wavelet-based Optical Velocimetry: an Alternative Technique for Deepwater Oil Spill Flow Rate Estimation. Indian J. Geo-Mar. Sci. 46(12): 2579-2587.
• Lupidi, A., Staglianò, D., Martorella, M., Berizzi, F., (2017). Fast Detection of Oil Spills and Ships Using SAR Images. Remote Sens. 9: 230.
• Song, D., Ding, Y., Li, X., Zhang, B., Xu,M., (2017). Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network. Remote Sens. 9: 799.
• Liu, B., Li, Y., Liu, C., Xie, F., Muller, J.P., (2018). Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction. Sensors. 18: 234.
• Huang, X.D., Wang, C.Y., Fan, X.M., Zhang, J.L., Yang, C., Wang, Z.D., (2018). Oil Source Recognition Technology using Concentration-synchronous-matrix-fluorescence Spectroscopy Combined with 2D Wavelet Packet and Probabilistic Neural Network. Sci. Total Environ. 616–617: 632-638.
• Mallat, S. (2009). A Wavelet Tour of Signal Processing, 3rd ed., Academic Press, New York.
• Jansen, M., Oonincx, P. (2005). Second Generation Wavelets and Applications, Springer – Verlag, London.
• Christopher, H., Walnut, D. F. (2006). Fundamental Papers in Wavelet Theory, Princeton University Press, London.
• Kingsbury, N.G., Magarey, J.F.A., 1997. Wavelet Transforms in Image Processing. Proc. First European Conference on Signal Analysis and Prediction, 24-27 June 1997, pp. 23 – 24, Prague, Czech Republic.
• Vujović, I., Šoda, J., Kuzmanić, I., (2012). Cutting-edge Mathematical Tools in Processing and Analysis of Signals in Marine and Navy. Trans. Marit. Sci. 1(1): 35-48.
• Chandrasekhar, E., Dimri, V. P., Gadre, V. M. (2014). Wavelets and Fractals in Earth System Sciences, CRC Press, Boca Raton.
• Mathworks, (2018). Image Processing Toolbox – User’s Guide, Mathworks, Natick, MA. Available at: https://www.mathworks.com/help/pdf_doc/images/images_tb.pdf