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Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm

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Języki publikacji
EN
Abstrakty
EN
In this work, a genetic algorithm is exploited for automatic detection of oil spills of small and large size. The route is achieved using arrays of RADARSAT-2 SAR ScanSAR Narrow single beam data obtained in the Gulf of Mexico. The study shows that genetic algorithm has automatically segmented the dark spot patches related to small and large oil spill pixels. This conclusion is confirmed by the receiveroperating characteristic (ROC) curve and ground data which have been documented. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the curve between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. The small oil spill sizes represented 30% of the discriminated oil spill pixels in ROC curve. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills of either small or large size and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill patterns detection and surveying in the Gulf of Mexico.
Czasopismo
Rocznik
Strony
1916--1941
Opis fizyczny
Bibliogr. 43 poz.
Twórcy
autor
  • Geospatial Information Science Research Centre, Faculty of Engineering, University Putra Malaysia, Serdang, Selangor, Malaysia
Bibliografia
  • Alpers, W. (2002), Remote sensing of oil spills. In: Proc. “Maritime Disaster Management” Symp., King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia, 19-23.
  • Brekke, C., and A. Solberg (2005), Oil spill detection by satellite remote sensing, Remote Sens. Environ. 95, 1, 1-13, DOI: 10.1016/j.rse.2004.11.015.
  • Caruso, M.J., M. Migliaccio, J.T. Hargrove, O. Garcia-Pineda, and H.C. Graber (2013), Oil spills and slicks imaged by synthetic aperture radar, Oceanography 26, 2, 112-123, DOI: 10.5670/oceanog.2013.34.
  • Cheng, A., M. Arkett, T. Zagon, R. De Abreu, D. Mueller, P. Vachon, and J. Wolfe (2011), Oil detection in RADARSAT-2 quad-polarization imagery: Implications for ScanSAR performance. In: Proc. SPIE 8179, SAR Image Analysis, Modeling, and Techniques XI, 19 September 2011, Prague, Czech Republic, 81790G, DOI: 10.1117/12.898358.
  • Choudhury, I., and M. Chakraborty (2006), SAR signature investigation of rice crop using RADARSAT data, Int. J. Remote Sens. 27, 3, 519-534, DOI: 10.1080/01431160500239172.
  • Cococcioni, M., L. Corucci, A. Masini, and F. Nardelli (2012), SVME: an ensemble of support vector machines for detecting oil spills from full resolution MODIS images, Ocean Dyn. 62, 3, 449-467, DOI: 10.1007/s10236-011- 0510-8.
  • Davis, L. (1991), The Handbook of Genetic Algorithms, Van Nostran Reingold, New York, 385 pp.
  • Fingas, M., and C. Brown (2014), Review of oil spill remote sensing, Mar. Pollut. Bull. 83, 1, 9-23, DOI: 10.1016/j.marpolbul.2014.03.059.
  • Fiscella, B., A. Giancaspro, F. Nirchio, P. Pavese, and P. Trivero (2000), Oil spill detection using marine SAR images, Int. J. Remote Sens. 21, 18, 3561- 3566, DOI: 10.1080/014311600750037589.
  • Frate, F.D., A. Petrocchi, J. Lichtenegger, and G. Calabresi (2000), Neural networks for oil spill detection using ERS-SAR data, IEEE Trans. Geosci. Remote Sens. 38, 5, 2282-2287, DOI: 10.1109/36.868885.
  • Gade, M., W. Alpers, H. Hühnerfuss, H. Masuko, and T. Kobayashi (1998), Imaging of biogenic and anthropogenic ocean surface films by the multifrequency/ multipolarization SIR-C/X-SAR, J. Geophys. Res. 103, C9, 18851-18866, DOI: 10.1029/97JC01915.
  • Garcia-Pineda, O., I.R. MacDonald, X. Li., C.R. Jackson, and W.G. Pichel (2013), Oil spill mapping and measurement in the Gulf of Mexico with Textural Classifier Neural Network Algorithm (TCNNA), IEEE J. STARS 6, 6, 1-9, DOI: 10.1109/JSTARS.2013.2244061.
  • Grimaldi, C.S.L., I. Coviello, T. Lacava, N. Pergola, and V. Tramutoli (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: Y. Liu, A. MacFadyen, Z.-G. Ji, and R.H. Weisberg (eds.), Monitoring and Modeling the Deepwater Horizon Oil Spill: A Record-Breaking Enterprise, American Geophysical Union, Washington, 19-31.
  • Guo, Y., and H.Z. Zhang (2014), Oil spill detection using synthetic aperture radar images and feature selection in shape space, Int. J. Appl. Earth Observ. Geoinf. 30, 146-157, DOI: 10.1016/j.jag.2014.01.011.
  • Ivanov, A., M.X. He, and M.Q. Fang (2002), Oil spill detection with the RADARSAT SAR in the waters of the Yellow and East China Sea: A case study. In: 23rd Asian Conference on Remote Sensing, 13-17 November 2002, Nepal, Asian Remote Sensing Society, Japan, 1, 1-8 (CD rom).
  • Kahlouche, S., K. Achour, and M. Benkhelif (2002), A new approach to image segmentation using genetic algorithm with mathematical morphology. In: Proc. 2002 WSEAS Int. Conf., 12-16 June 2002, Cadiz, Spain, 1-5, available from: http://www.wseas.us/elibrary/conferences/spain2002/papers/443- 164.pdf.
  • Lounis, B., and A. Belhadj-Aissa (2014), Sea SAR images analysis to detect oil slicks in Algerian coasts, J. Math. Modell. Algorithms Operations Res. 83, 1, 9-23, DOI: 10.1007/s10852-014-9250-3.
  • Marghany, M. (2001), RADARSAT automatic algorithms for detecting coastal oil spill pollution, Int. J. Appl. Earth Observ. Geoinf. 3, 2, 191-196, DOI: 10.1016/S0303-2434(01)85011-X.
  • Marghany, M. (2013), Genetic algorithm for oil spill automatic detection from ENVISAT satellite data. In: B. Murgante, S. Misra, M. Carlini, C.M. Torre, H.-Q. Nguyen, D. Taniar, B.O. Apduhan, and O. Gervasi (eds.), Computational Science and Its Applications – ICCSA 2013, Lecture Notes in Computer Science, Vol. 7972, Springer, Berlin Heidelberg, 587-598, DOI: 10.1007/978-3-642-39643-4_42.
  • Marghany, M. (2015), Multi-objective entropy evolutionary algorithm for marine oil spill detection using cosmo-skymed satellite data, Ocean Sci. Discuss. 12, 3, 1263-1289, DOI: 10.5194/osd-12-1263-2015.
  • Marghany, M., and M. Hashim (2011), Comparative algorithms for oil spill detection from multi mode RADARSAT-1 SAR satellite data. In: B. Mur-gante, O. Gervasi, A. Iglesias, D. Taniar, and B.O. Apduhan (eds.). Computational Science and Its Applications – ICCSA 2011, Lecture Notes in Computer Science, Vol. 6783, 318-329, DOI: 10.1007/978-3-642-21887- 3_25.
  • Marghany, M., A.P. Cracknell, and M. Hashim (2009), Modification of fractal algorithm for oil spill detection from RADARSAT-1 SAR data, Int. J. Appl. Earth Observ. Geoinf. 11, 2, 96-102, DOI: 10.1016/j.jag.2008.09.002.
  • MDA (2009), RADARSAT-2 product description, MacDonald, Dettwiler and Associates Ltd., available from: http://www.gs.mdacorporation.com (accessed: 7 March 2014).
  • Michalewicz, Z. (1994), Genetic Algorithms + Data Structures = Evolution Programs, 2nd ed., Springer Verlag, New York, 340 pp.
  • Mohanta, R.K., and B. Sethi (2011), A review of genetic algorithm application for image segmentation, Int. J. Comput. Technol. Appl. 3, 2, 720-723.
  • NOAA OR&R (2013), Deepwater horizon trajectory map archive, National Oceanic and Atmospheric Administration, Washington, DC, USA, available from: http://archive.orr.noaa.gov (accessed: 23 October 2013).
  • NOAA/NESDIS (2013), National environmental satellite information service, experimental marine pollution surveillance daily composite product, National Oceanic and Atmospheric Administration, Washington, DC, USA, available from: http://satepsanone.nesdis.noaa.gov/OMS/disasters/Deepwater Horizon/composites/2010/ (accessed: 8 August 2014).
  • RADARSAT-2 (2014), Satellite characteristics, Canadian Space Agency, available from: http://www.asc-csa.gc.ca/eng/satellites/radarsat/radarsat-tableau.asp (accessed: 7 March 2014).
  • Shay, L.K., B. James, J.K. Brewster, P. Meyers, E. Claire, E.C. McCaskill, E. Uhlhorn, F. Marks, G.R. Halliwell Jr., O. Martin, O.M. Smedstad, and P. Hogan (2011), Airborne ocean surveys of the loop current complex from NOAA WP-3D in support of the “Deepwater Horizon” oil spill. In: Y. Liu, A. MacFadyen, Z.-G. Ji, and R.H. Weisberg (eds.), Monitoring and Modeling the Deepwater Horizon Oil Spill: A Record-Breaking Enterprise, American Geophysical Union, Washington, D.C., 131-151, DOI: 10.1029/ 2011GM001101.
  • Shirvany, R., M. Chabert, and J.-Y. Tourneret (2012), Ship and oil-spill detection using the degree of polarization in linear and hybrid/compact dual-pol SAR, IEEE J. STARS 5, 3, 885-892, DOI: 10.1109/JSTARS.2012.2182760.
  • Sivanandam, S.N, and S.N. Deepa (2008), Introduction to Genetic Algorithms, Springer, Berlin Heidelberg.
  • Skrunes, S., C. Brekke, and T. Eltoft (2012), An experimental study on oil spill characterization by multi-polarization SAR. In: Proc. 9th European Conf. on Synthetic Aperture Radar, 23-26 April 2012, Nuremberg, Germany, 139- 142.
  • Topouzelis, K., V. Karathanassi, P. Pavlakis, and D. Rokos (2007), Detection and discrimination between oil spills and look-alike phenomena through neural networks, ISPRS J. Photogram. Remote Sens. 62, 4, 264-270, DOI: 10.1016/j.isprsjprs.2007.05.003.
  • Topouzelis, K., D. Stathakis, and V. Karathanassi (2009a), Investigation of genetic algorithms contribution to feature selection for oil spill detection, Int. J. Remote Sens. 30, 3, 611-625, DOI: 10.1080/01431160802339456.
  • Topouzelis, K., V. Karathanassi, P. Pavlakis, and D. Rokos (2009b), Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes, Geocarto Int. 24, 3, 179-191, DOI: 10.1080/ 10106040802488526.
  • Velotto, D., M. Migliaccio, F. Nunziata, and S. Lehner (2011), Dual-polarized terraSAR-X data for oil-spill observation, IEEE Trans. Geosci. Remote Sens. 49, 12, 4751-4762, DOI: 10.1109/TGRS.2011.2162960.
  • Walker, N.D., C.T. Pilley, V.V. Raghunathan, E.J. D’Sa, R.R. Leben, N.G. Hoffmann, P.J. Brickley, P.D. Coholan, N. Sharma, H.C. Graber, and R.E. Turner (2011), Impacts of loop current frontal cyclonic eddies and wind forcing on the 2010 Gulf of Mexico oil spill. In: Y. Liu, A. MacFadyen, Z.-G. Ji, and R.H. Weisberg (eds.), Monitoring and Modeling the Deepwater Horizon Oil Spill: A Record-Breaking Enterprise, American Geophysical Union, Washington, DC, 103-116, DOI: 10.1029/2011GM001120.
  • Xu, L., J. Li, and A. Brenning (2014), A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery, Remote Sens. Environ. 141, 14-23, DOI: 10.1016/j.rse.2013.10. 012.
  • Zangari, G., (2010), Risk of global climate change by BP oil spill, available from: http://www.associazionegeofisica.it/OilSpill.pdf (accessed: 7 March 2014).
  • Zhang, B., W. Perrie, X. Li, and W. Pichel (2011), Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image, Geophys. Res. Lett. 38, 10, L10602, DOI: 10.1029/2011GL047013.
  • Zhang, Y., H. Lin, Q. Liu, J. Hu, X. Li, and K. Yeung (2012), Oil-spill monitoring in the coastal waters of Hong Kong and vicinity, Mar. Geod. 35, 1, 93-106, DOI: 10.1080/01490419.2011.637872.
  • Zhang, Y., Y. Li, and H. Lin (2014), Oil-spill pollution remote sensing by synthetic aperture radar. In: M. Marghany (ed.), Advanced Geoscience Remote Sensing, InTech, Rijeka, 27-50, DOI: 10.5772/57477, available from: http://www.intechopen.com/books/advanced-geoscience-remote-sensing/oil-spillpollution-remote-sensing-by-synthetic-aperture-radar (accessed: 7 August 2014).
  • Zhao, J., M. Temimi, H. Ghedira, and C. Hu (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, 11, 13755-13772, DOI: 10.1364/OE.22.013755.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9cc9ae05-3581-41ff-899d-93177e68faee
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