Spaceborne radar remote sensing of ocean surfaces: electromagnetic modelling and applications

References

• Alfoldi TT, Prout NA. The use of satellite data for monitoring oil spills in Canada. Environment Canada Report; 1982. (Tech. Rep. 3-EC-82-5).
   Google Scholar
• Fingas MF, Brown EC. Review of oil spill remote sensing. Spill Sci Technol Bull. 1997;4(4):199–208.
   Google Scholar
• Palenzuela JMT, Vilas LG, Cuadrado MS. Use of Asar images to study the evolution of the prestige oil spill off the Galician coast. Int J Remote Sens. 2006;27(9–10):1931–1950.
   Web of Science ®Google Scholar
• Fingas M. Springer handbook of ocean engineering, Vol. Part D – Offshore Technologies, ch. Oil Spills and Response. Cham, Switzerland: Springer; 2016. p. 1067–1094.
   Google Scholar
• Sankaran K, Fortuny-Guasch J. Radar remote sensing for oil spill classification (optimization for enhanced classification). Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference; Dubrovnik, Croatia. 2004. p. 511–514. DOI:10.1109/MELCON.2004.1346979.
   Google Scholar
• Bhatt A, Aakash, Sankaran K. Spaceborne ocean monitors: radar imaging of illicit oil-spills. IEEE-INAE Workshop on Electromagnetics IIWE, Trivandrum, India, Indian National Academy of Engineering; 2018.
   Google Scholar
• Oliver C, Quegan S. Understanding synthetic aperture radar. Raleigh (North Carolina): SciTech Publishing; 2004.
   Google Scholar
• Alpers W. Imaging ocean surface waves by synthetic aperture radar—A review. In: Allan TD, editor. Satellite microwave remote sensing. Chichester: Ellis Horwood Ltd; 1983. p. 107–119.
   Google Scholar
• Hasselmann K, Raney RK, Plant WJ, et al. Theory of synthetic aperture radar ocean imaging: A MARSEN view. J Geophys Res. 1985;90(C3):4659–4686.
   Google Scholar
• Fan K, Zhang Y, Lin H. Satellite SAR analysis and interpretation of oil spill in the offshore water of Hong Kong. Ann GIS. 2010;16(4):269–275.
   Google Scholar
• Skolnik M. Spaceborne radar for the global surveillance of ships over the ocean. IEEE National Radar Conference. Vol. 2; 1997. p. 120–125.
   Google Scholar
• Trivero P, Fiscella B, Gomez F, et al. SAR detection and characterization of sea surface slicks. Int J Remote Sens. 1998;19(3):543–548.
   Web of Science ®Google Scholar
• Berkke C, Solberg AHS. Oil spill detection by satellite remote sensing. Remote Sens Environ. 2005;95(1):1–13.
   Web of Science ®Google Scholar
• Alpers W. Radar signatures of oil films floating on the sea surface and the Marangoni effect. J Geophys Res. 1988;93(C4):3642–3648.
   Web of Science ®Google Scholar
• Alpers W, Holt B, Zeng K. Oil spill detection by imaging radars: challenges and pitfalls. Remote Sens Environ. 2017;201:133–147.
   Web of Science ®Google Scholar
• Fiscella B, Giancaspro A, Nirchio F, et al. Oil spill detection using marine SAR images. Int J Remote Sens. 2000;21(18):3561–3566.
   Web of Science ®Google Scholar
• Espedal HA, Johannessen OM. Cover: detection of oil spills near offshore installations using synthetic aperture radar (SAR). Int J Remote Sens. 2000;21(11):2141–2144.
   Web of Science ®Google Scholar
• Guinard N. Radar detection of oil spills. Joint Conference on Sensing of Environmental Pollutants; 1971.
   Google Scholar
• Espedal H. Oil spill and its look-alikes in ERS SAR imagery. Earth Obs Remote Sens. 1998;5:94–102.
   Google Scholar
• Sankaran K. Protecting oceans from illicit oil spills: environment control and remote sensing using spaceborne imaging radars. J Electromagnet Waves Appl. 2019;33(18). DOI:10.1080/09205071.2019.1685409
   Web of Science ®Google Scholar
• Immoreev IJ, Taylor JD. Future of radars. IEEE Conference on Ultra Wideband Systems and Technologies; 2002. p. 197–199.
   Google Scholar
• Okamoto K, Kobayashi T, Masuko H, et al. Results of experiments using synthetic aperture radar onboard the European remote sensing satellite 1-4, artificial oil pollution detection. J Commun Res Lab. 1996;43(3):327–344.
   Google Scholar
• Pavlakis P, Tarchi D, Sieber AJ. On the monitoring of Illicit vessel discharges, a reconnaissance study in the mediterranean sea. Annales des Télécommunications. 2001;56(11-12):700–718.
   Web of Science ®Google Scholar
• Pavlakis P, Sieber A, Alexandry S. Monitoring oil-spill pollution in the mediterranean with ERS SAR. Earth Obs Quaterly. 1996;52:8–11. ESA, Technical Report.
   Google Scholar
• Lombardo P, Oliver CJ. Optimum detection and segmentation of oil-slicks using polarimetric SAR data. IEE Proceedings on Radar, Sonar and Navigation. Vol. 147; 2000. p. 309–320.
   Google Scholar
• Lombardo P, Oliver CJ. Optimum detection and segmentation of oil-slicks using polarimetric SAR data. IEE Proc. – Radar, Sonar Navigation. 2000;147(6):309–321.
   Google Scholar
• Bertacca M, Berizzi F, Mese ED. A FARIMA-based technique for oil slick and low-wind areas discrimination in sea sar imagery. IEEE Trans Geosci Remote Sens. 2005;43(11):2484–2493.
   Web of Science ®Google Scholar
• Akkartal A, Sunar F. The use of radar images in oil spill detection. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; 2008. p. 271–276.
   Google Scholar
• Topouzelis KN. Oil spill detection by SAR images: dark formation detection, feature extraction and classification algorithms. Sensors. 2008;8(10):6642–6659.
   PubMed Web of Science ®Google Scholar
• Kubat M, Holte RC, Matwn S. Machine learning for the detection of oil spills in satellite radar images. Mach Learn. 1998;3(2):195–215.
   Google Scholar
• Solberg ARS, Storvik G, Solberg R, et al. Automatic detection of oil spills in ERS SAR images. IEEE Trans Geosci Remote Sens. 1999;37(4):1919–1924.
   Web of Science ®Google Scholar
• Farte FD, Petrocchi A, Lichtenegger J. Neural networks for oil spill detection using ERS-SAR data. IEEE Trans Geosci Remote Sens. 2000;38(5):2282–2287.
   Web of Science ®Google Scholar
• Novak LM, Sechtin MB, Cardullo MJ. Studies of target detection algorithms that use polarimetric radar data. IEEE Trans Aerosp Electron Syst. 1989;25(2):150–165.
   Web of Science ®Google Scholar
• Solberg R, Theophilopoulos N. ENVISYS – a solution for automatic oil spill detection in the mediterranean. Fourth Thematic Conference on Remote Sensing for Marine and Coastal Environments, ERIM. Vol. 1; 1997. p. 3–12.
   Google Scholar
• Ulaby FT, Elachi C. Radar polarimetry for geoscience applications. Norwood (MA): Artech House, Inc.; 1990.
   Google Scholar
• Elachi C. Spaceborne radar remote sensing: applications and techniques. New York: IEEE Press; 1988.
   Google Scholar
• Hühnerfuss H, Alpers W, Gericke A. Classification of sea slicks by multi-frequency radar techniques: the influence of the temperature and of the morphology effect on water wave damping. Proceedings of OCEANS'93, ‘Engineering in Harmony with Ocean’. Vol. 2; 1993. p. 336–341.
   Google Scholar
• Elachi C, Zyl JJV. Introduction to the physics and techniques of remote sensing. 2nd ed. Hoboken (NJ): John Wiley & Sons, Inc.; 2006.
   Google Scholar
• Campbell JB, Wynne RH. Introduction to remote sensing. 5th ed. New York: The Guilford Press; 2011.
   Google Scholar
• Zebker HA, van Zyl JJ, Held DN. Imaging radar polarimetry from wave synthesis. J Geophys Res. 1987;92(B1):683–701.
   Web of Science ®Google Scholar
• Kennaugh EM. Polarization dependence of RCS – a geometrical interpretation. IEEE Trans Antennas Propag. 1981;29(2):412–413.
   Web of Science ®Google Scholar
• Santalla V, McCormick GC, Antar YMM. Optimal polarizations and application to meteorological targets. IEEE Trans Antennas Propag. 1999;47(4):767–769.
   Web of Science ®Google Scholar
• Wheeler GJ. Introduction to microwaves. 2nd ed. Englewood Cliffs (NJ): Printice-Hall, Inc.; 1964.
   Google Scholar
• Huynen JR. Phenomenological theory of radar targets [PhD thesis]. The Netherlands: Technical University of Delft; 1970.
   Google Scholar
• Hubbert JC, Bringi VN. Studies of the polarimetric covariance matrix. Part II: modeling and polarization errors. J Atmos Oceanic Technol. 2003;20(7):1011–1022.
   Web of Science ®Google Scholar
• Boerner WM, Kostinski AB, James BD. Application of the polarimetric matched image filter (PMIF) technique to clutter removal in POL-SAR images of the ocean environment. Proceedings of OCEANS'88, ‘A Partnership of Marine Interests’. Vol. 2; 1988. p. 454–461.
   Google Scholar
• Mott H. Polarimetric contrast optimization. International Geoscience and Remote Sensing Symposium, IGARSS'95. Quantitative Remote Sensing for Science and Applications. Vol. 3; 1995. p. 2011–2013.
   Google Scholar
• Gade M, Alpers W, Bao M, et al. Measurements of the radar backscattering over different oceanic surface films during the SIR-C/X-SAR campaigns. IEEE Proceedings of the International Geoscience and Remote Sensing Symposium IGARSS. Vol. 2; 1996, pp. 860–862.
   Google Scholar
• Novak LM, Burl MC. Optimal speckle reduction in polarimetric SAR imagery. Trans Aeros Electron Syst. 1990;26(2):293–305.
   Web of Science ®Google Scholar
• Barnes RM. Detection of a randomly polarized target [PhD thesis]. Boston, USA: Northeastern University; 1984.
   Google Scholar
• Sankaran K. Are you using the right tools in computational electromagnetics? Engineering Reports. 2019;1(3):1–19. DOI:10.1002/eng2.12041.
   Google Scholar
• Howard D, Schroeder J. Multiscale models for target detection and background discrimination in synthetic aperture radar imagery. Digit Signal Process. 1999;9(3):149–161.
   Web of Science ®Google Scholar
• Schroeder J, Howard D, Gunawardena A. Multiscale modelling for target detection in complex synthetic aperture radar imagery. IEEE, Information, Decision and Control Proceedings, Adelaide, Australia, February 1999. p. 77–82.
   Google Scholar
• Sankaran K. Recent trends in computational electromagnetics for defence applications. Defence Science Journal. 2019;69(1):65–73. DOI:10.14429/dsj.69.13275.
   Web of Science ®Google Scholar
• Uchida K, Yoon K-Y, Kuga Y, et al. FVTD analysis of electromagnetic wave scattering by rough surface. IGARSS'98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. Vol. 4, July 1998. p. 2292–2294.
   Google Scholar
• Xu X, Brekke C, Doulgeris A. Numerical analysis of microwave scattering from layered sea ice based on the finite element method. Remote Sens (Basel). 2018 Aug;10:1–16.
   Web of Science ®Google Scholar
• Fumeaux C, Baumann D, Sankaran K, et al. The finite-volume time-domain method for 3-D solutions of Maxwell's equations in complex geometries: a review. Proceedings of the European Microwave Association. Vol. 3, EuMA; 2007. p. 136–146.
   Google Scholar
• Fumeaux C, Almpanis G, Sankaran K. Finite-volume time-domain modeling of the mutual coupling between dielectric resonator antennas in array configurations. The Second European Conference on Antennas and Propagation (EuCAP), IET; 2007. p. 1–4. DOI:10.1049/ic.2007.0909.
   Google Scholar
• Kaufmann T, Sankaran K, Fumeaux C, et al. A review of perfectly matched absorbers for the finite-volume time-domain method. Appl Comput Electromagnet Soc. 2008;23(3):184–192.
   Web of Science ®Google Scholar
• Sankaran K, Kaufmann T, Fumeaux C, et al. Different perfectly matched absorbers for conformal time-domain method: a finite-volume time-domain perspective. 23rd Annual Review of Progress in Applied Computational Electromagnetics, ACES; 2007. p. 1712–1718.
   Google Scholar
• Sankaran K, Fumeaux C, Vahldieck R. Finite-volume Maxwellian absorbers on unstructured grid. IEEE MTT-S International Microwave Symposium Digest; San Francisco, California, US. 2006. p. 169–172. DOI:10.1109/MWSYM.2006.249422.
   Google Scholar
• Sankaran K, Fumeaux C, Vahldieck R. Radial absorbers for conformal time-domain methods: a solution to corner problems in mesh truncation. IEEE/MTT-S International Microwave Symposium; Honolulu, Hawaii, USA. 2007. p. 709–712. DOI:10.1109/MWSYM.2007.380019.
   Google Scholar
• Sankaran K, Fumeaux C, Vahldieck R. An investigation of the accuracy of finite-volume radial domain truncation technique. Workshop on Computational Electromagnetics in Time Domain, IEEE; Perugia, Italy. 2007. p. 1–4. DOI:10.1109/CEMTD.2007.4373520.
   Google Scholar
• Sankaran K, Fumeaux C, Vahldieck R. Hybrid PML-ABC truncation techniques for finite-volume time-domain simulations. Proceedings of the Asia-Pacific Microwave Conference, APMC; Yokohama, Japan. 2006. p. 949–952. DOI:10.1109/APMC.2006.4429569.
   Google Scholar
• Sankaran K, Fumeaux C, Vahldieck R. Split and unsplit finite-volume absorbers: formulation and performance comparison. Proceedings of the European Microwave Conference, EuMA; Manchester, UK. 2006. p. 17–20. DOI:10.1109/EUMC.2006.281170.
   Google Scholar
• Aakash, Bhatt A, Sankaran K. Transcending limits: recent trends & challenges in computational electromagnetics. IEEE-INAE Workshop on Electromagnetics IIWE, Trivandrum, India, Indian National Academy of Engineering; 2018.
   Google Scholar
• Aakash, Bhatt A, Sankaran K. Algebraic topological method: an alternative modelling tool for electromagnetics. 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC); New Delhi, India. March 2019. DOI:10.23919/URSIAP-RASC.2019.8738433.
   Google Scholar
• Sankaran K, Sairam B. Modelling of nanoscale quantum tunnelling structures using algebraic topology method. AIP Conference Proceedings. Vol. 1953; 2018. p. 140105–1–140105–4. DOI:10.1063/1.5033280.
   Google Scholar
• Sankaran K. Beyond DIV, CURL and GRAD: modelling electromagnetic problems using algebraic topology. J Electromagn Waves Appl. 2017;31(2):121–149. DOI:10.1080/09205071.2016.1257397.
   Web of Science ®Google Scholar
• Aakash, Bhatt A, Sankaran K. How to model electromagnetic problems without using vector calculus and differential equations? IETE Journal of Education. 2018;59(2):85–92. DOI:10.1080/09747338.2018.1554456.
   Google Scholar
• Ziemke T. Radar image segmentation using recurrent artificial neural networks. Artificial Neural Networks in Computer Vision, Pattern Recognition Letters; 1996.
   Google Scholar