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Automatic detection of oil spills in the Gulf of Mexico from RADARSAT-2 SAR satellite data

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Abstract

This work aimed to design optimization procedures for oil spill footprint automatic detection from synthetic aperture radar (SAR) satellite data. The main motivation of this work is to utilize a genetic algorithm (GA) without involving post-classification image processing tools for oil spill footprint boundary shape optimizations that involve local and global optimizations. The procedures are operated using sequences of RADARSAT-2 SAR ScanSAR Narrow single beam data acquired in the Gulf of Mexico. The study shows that the GA has high performance for oil spill boundary shape automatic optimization and detection. This provides evidence with standard error of 0.12 and non-significant differences with different acquisition dates. The ScanSAR Narrow mode data shows the extremely existing of 90 % of the oil spill footprint compared to the sea surface roughness and look-alikes. It can be said that Scan SAR Narrow mode can monitor oil spill disasters. In conclusion, the GA can be used as an automatic tool for oil spill without involving other post-image processing classification.

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References

  • Alpers W (2002) Remote sensing of oil spills, In: Proceedings of the symposium “Maritime Disaster Management”. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia, pp 19–23

  • Brekke C, Solberg A (2005) Oil spill detection by satellite remote sensing: remote sensing of environment 95:1–13

    Google Scholar 

  • Caruso MJ, Migliaccio M, Hargrove JT, Garcia-Pineda O, Graber HC (2013) Oil spills and slicks imaged by synthetic aperture radar. Oceanography 26(2):112–123

    Article  Google Scholar 

  • Cheng A, Arkett M, Zagon T, De Abreu R, Mueller D, Vachon P, Wolfe J (2011) Oil detection in RADARSAT-2 quad-polarization imagery: Implications for ScanSAR performance. In: Proceedings SPIE 8179, SAR image analysis, modeling, and techniques XI, 81790G. Accessed 26 Oct 2011

  • Choudhury I, Chakraborty M (2006) SAR signature investigation of rice crop using RADARSAT data. Int J Remote Sens 27:519–534

    Article  Google Scholar 

  • Cloude SR, Pottier E (1996) A review of target decomposition theorems in radar polarimetry. IEEE Trans Geosci Remote Sens 34(2):498–518

    Article  Google Scholar 

  • Cococcioni M, Corucci L, Masini A, Nardelli F (2012) SVME: an ensemble of support vector machines for detecting oil spills from full resolution MODIS images. Ocean Dyn 62:449–467

    Article  Google Scholar 

  • Davis L (1991) The handbook of genetic algorithms. Van Nostran Reingold, New York

    Google Scholar 

  • Fingas M, Brown C (2014) Review of oil spill remote sensing. Mar Pollut Bull 83(1):9–23

    Article  Google Scholar 

  • Fiscella B, Giancaspro A, Nirchio F, Pavese P, Trivero P (2000) Oil spill detection using marine SAR images. Int J Remote Sens 21:3561–3566

    Article  Google Scholar 

  • Frate FD, Petrocchi A, Lichtenegger J, Calabresi G (2000) Neural networks for oil spill detection using ERS-SAR data. IEEE Trans Geosci Remote Sens 38:2282–2287

    Article  Google Scholar 

  • Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic Press, New York

    Google Scholar 

  • Gade M, Alpers W, Hühnerfuss H, Masuko H, Kobayashi T (1998) Imaging of biogenic and anthropogenic ocean surface films by the multifrequency/multipolarization SIR-C/X-SAR. J Geophys Res 103(C9):18851–18866

    Article  Google Scholar 

  • Garcia-Pineda O, MacDonald IR, Li X, Jackson CR, Pichel WG (2013) Oil spill mapping and measurement in the Gulf of Mexico with textural classifier neural network algorithm (TCNNA). Sel Topics Appl Earth Observ Remote Sens 99:1–9

    Google Scholar 

  • Grimaldi CSL, Coviello I, Lacava T, Pergola N, Tramutoli V (2011) A new RST-based approach for continuous oil spill detection in TIR range: the case of the Deepwater Horizon Platform in the Gulf of Mexico. In: Liu Y, Macfadyen A, Ji Z-G, Weisberg RH (eds) Monitoring and modeling the deepwater horizon oil spill: a record-breaking enterprise. American Geophysical Union, Washington, DC, pp 19–31

    Chapter  Google Scholar 

  • Guo Y, Zhang HZ (2014) Oil spill detection using synthetic aperture radar images and feature selection in shape space Original Research Article. Int J Appl Earth Obs Geoinf 30:146–157

    Article  Google Scholar 

  • Ivanov A, He M, Fang MQ (2002) Oil spill detection with the RADARSAT SAR in the waters of the Yellow and East Sea: a case study: CD of 23rd Asian conference on remote sensing, 13–17 Nov 2002, Nepal, Asian Remote Sensing Society, Japan. 1: 1–8

  • Kahlouche S, Achour K, Benkhelif M (2002) A new approach to image segmentation using genetic algorithm with mathematical morphology. In: Proceedings of the 2002 WSEAS International Conferences, Cadiz, Spain, June 12–16 2002. http://www.wseas.us/e-library/conferences/spain2002/papers/443-164.pdf, pp 1–5

  • Lombardini PP, Fiscella B, Trivero P, Cappa C, Garrett WD (1989) Modulation of the spectra of short gravity waves by sea surface films: slick detection and characterization with microwave probe. J Atmos Ocean Technol 6:882–890

    Article  Google Scholar 

  • Lounis B, Belhadj-Aissa A (2014) Sea SAR images analysis to detect oil slicks in Algerian coasts. J Math Model Algorithms Op Res. http://link.springer.com.ezproxy.psz.utm.my/article/10.1007/s10852-014-9250-3. Accessed 7 Aug 2014

  • Lynn KS, Benjamin J, Jodi KK, Patrick M, McCaskill EC, Eric U, Frank M, George RH Jr, Ole MS, Patrick H (2011) Airborne ocean surveys of the loop current complex from NOAA WP-3D in support of the deepwater horizon oil spill. In: Liu Y, Macfadyen A, Ji Z-G, Weisberg RH (eds) Monitoring and modeling the deepwater horizon oil spill: a record-breaking enterprise. American Geophysical Union, Washington, DC, pp 131–151

    Google Scholar 

  • Marghany M (2001) RADARSAT automatic algorithms for detecting coastal oil spill pollution. Int J Appl Earth Obs Geoinf 3:191–196

    Article  Google Scholar 

  • Marghany M (2013) Genetic algorithm for oil spill automatic detection from Envisat Satellite Data. In: Murgante B, Misra S, Carlini M, Torre CM, Nguyen H-Q, Taniar D, Apduhan BO, Gervasi O (eds) Computational science and its applications—ICCSA 2013. Springer Berlin Heidelberg, pp 587–598

  • Marghany M, Hashim M (2011a) Comparative algorithms for oil spill detection from multi mode RADARSAT-1 SAR satellite data. Lecture Notes in Computer Science. In: Taniar et al. (Eds.). Computational Science and Its Applications—ICCSA 2011, 6783: 318–329

  • Marghany M, Hashim M (2011b) Comparison between Mahalanobis classification and neural network for oil spill detection using RADARSAT-1 SAR data. Int J Phys Sci 6(3):566–576

    Google Scholar 

  • Marghany M, van Genderen J (2014) Entropy algorithm for automatic detection of oil spill from radarsat-2 SAR data. 8th International Symposium of the Digital Earth (ISDE8. IOP Conf Ser 18(2014):012051. doi:10.1088/1755-1315/18/1/012051

    Article  Google Scholar 

  • Marghany M, Cracknell AP, Hashim M (2009) Modification of fractal Algorithm for oil spill detection from RADARSAT-1 SAR data. Int J Appl Earth Obs Geoinf 11:96–102

    Article  Google Scholar 

  • MDA (2009) RADARSAT-2 product description. http://www.gs.mdacorporation.com. Accessed on March 7 2014

  • MDA (2011) Marine environmental surveillance improvements with RADARSAT-2, GSI 191, June 2011. http://www.gs.mdacorporation.com. Accessed 7 March 2014

  • Michalewicz Z (1994) Genetic algorithms + data structures. Evolution Programs. Springer, New York

    Book  Google Scholar 

  • Minchew B, Jones C, Holt B (2012) Polarimetric analysis of backscatter from the deepwater horizon oil spill using L-band synthetic aperture radar. IEEE Trans Geosci Remote Sens 50(10):2012

    Article  Google Scholar 

  • Mohamed IS, Salleh AM, Tze LC (1999) Detection of oil spills in Malaysian waters from RADARSAT Synthetic Aperture Radar data and prediction of oil spill movement. In: Proceeding of 19th Asian conference on remote sensing, China, Hong Kong, 23–27 Nov 1999, Asian Remote Sensing Society, Japan, vol 2, pp 980–987

  • Mohanta RK, Sethi B (2011) A review of genetic algorithm application for image segmentation. Int J Comput Technol Appl 3(2):720–723

    Google Scholar 

  • Nan DW, Chet TP, Vandana VR, D’Sa Eurico J, Robert RL, Nicholas GH, Peter JB, Patrice DC, Neha S, Hans CG, Raymond E (2011) Turner impacts of loop current Frontal cyclonic eddies and wind forcing on the 2010 Gulf of Mexico oil spill. In: Liu Y, Macfadyen A, Ji Z-G, Weisberg RH (eds) Monitoring and modeling the deepwater horizon oil spill: a record-breaking enterprise. American Geophysical Union, Washington, DC, pp 103–116

    Google Scholar 

  • NOAA OR&R (2013) Deepwater Horizon trajectory map archive. Web Document. http://archive.orr.noaa.gov. Accessed 7 Aug 2014

  • NOAA/NESDIS (2013) National environmental satellite information service, experimental marine pollution surveillance daily composite product. Digital Archive. http://satepsanone.nesdis.noaa.gov/OMS/disasters/DeepwaterHorizon/composites/2010/. Accessed 8 Aug 2014

  • RADARSAT-2 (2014) Satellite characteristics. http://www.asccsa.gc.ca/eng/satellites/radarsat/radarsat-tableau.asp. Accessed 7 March 2014

  • Shirvany R, Chabert M, Tourneret J-Y (2012) Ship and oil-spill detection using the degree of polarization in linear and hybrid/compact dual-pol SAR. Sel Topics Appl Earth Observ Remote Sens 5:885–892

    Article  Google Scholar 

  • Sivanandam SN, Deepa SN (2008) Introduction to genetic algorithms. Springer, Berlin Heidelberg New York

    Google Scholar 

  • Skrunes S, Brekke C, Eltoft T (2012) An experimental study on oil spill characterization by multi-polarization SAR. In: Proceedings of European conference on synthetic aperture radar, Nuremberg, Germany, pp 139–142

  • Staples G, Rodrigues DF (2013) Maritime environmental surveillance with RADARSAT-2. Anais XVI Simpósio Brasileiro de Sensoriamento Remoto - SBSR, Foz do Iguaçu, PR, Brasil, 13 a 18 de abril de 2013, INPE. http://www.dsr.inpe.br/sbsr2013/files/p1061.pdf

  • Staples G, Touzi R (2014) The Application of RADARSAT-2 quad-polarized data for oil slick characterization. In: Proceedings of international oil spill conference, May 2014, vol 2014, No. 1, pp 2242–2252

  • Topouzelis K, Karathanassi V, Pavlakis P, Rokos D (2007) Detection and discrimination between oil spills and look-alike phenomena through neural networks. ISPRS J Photogrametry Remote Sens 62:264–270

    Article  Google Scholar 

  • Topouzelis K, Karathanassi V, Pavlakis P, Rokos D (2009a) Potentiality of feed forward neural networks for classifying dark formations to oil spills and look-alikes. Geocarto Int 24:179–191

    Article  Google Scholar 

  • Topouzelis K, Stathakis D, Karathanassi V (2009b) Investigation of genetic algorithms contribution to feature selection for oil spill detection. Int J Remote Sens 30(3):611–625

    Article  Google Scholar 

  • Velotto D, Migliaccio M, Nunziata F, Lehner S (2011) Dual-polarized TerraSAR-X data for oil-spill observation. IEEE Trans Geosci Remote Sens 49:4751–4762

    Article  Google Scholar 

  • Xu L, Li J, Brenning A (2014) A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery. Remote Sens Environ 141:14–23

    Article  Google Scholar 

  • Zangari G (2010) Risk of global climate change by BP oil spill. http://www.associazionegeofisica.it/OilSpill.pdf. Accessed 7 March 2014

  • Zhang B, Perrie W, Li X, Pichel W (2011) Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image. Geophys ResLett 38:L10602

    Google Scholar 

  • Zhang Y, Lin H, Liu Q, Hu J, Li X, Yeung K (2012) Oil-spill monitoring in the coastal waters of Hong Kong and vicinity. Mar Geodesy 35:93–106

    Article  Google Scholar 

  • Zhang Y, Li Y, Lin H (2014) Oil-spill pollution remote sensing by synthetic aperture Radar. In: Marghany M (ed) Advanced geoscience remote sensing. Intech, Croatia, pp 27–50

    Google Scholar 

  • Zhao J, Temimi M, Ghedira H, Hu C (2014) Exploring the potential of optical remote sensing for oil spill detection in shallow coastal waters-a case study in the Arabian Gulf. Opt Express 22:13755–13772

    Article  Google Scholar 

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  1. Institute of Geospatial Science and Technology (INSTeG), Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor Bahru, Malaysia

    Maged Marghany

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Marghany, M. Automatic detection of oil spills in the Gulf of Mexico from RADARSAT-2 SAR satellite data. Environ Earth Sci 74, 5935–5947 (2015). https://doi.org/10.1007/s12665-015-4617-y

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