Abstract
As radar backscatter values for oil slicks are very similar to backscatter values for very calm sea areas and other ocean phenomena, dark areas in Synthetic Aperture Radar (SAR) imagery tend to be misinterpreted. In this paper three feature sets are used to identify the oil slicks in SAR images. These images are submitted to different MLP architectures to verify the separability performance over each feature set. This analysis is very suitable for remote sensing of environment applications concerning marine oil pollution. The estimated resulting performance points out which feature set is the best suitable for the suggested application.
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References
Marghany, M.: Radarsat automatic algorithms for detecting coastal oil spill pollution. Asian Journal of Geoinformatics 3, 191–196 (2001)
Frate, F.D., Salvatori, L.: Oil spill detection by means of neural networks algorithms: a sensitivity analysis. In: IEEE International Geoscience and Remote Sensing Symposium, vol. 2, pp. 1370–1373 (2004)
Calabresi, G., Frate, F.D., Lichtenegger, J., Petrocchi, A.: Neural networks for the oil spill detection. In: IEEE International Geoscience and Remote Sensing Symposium, vol. 1, pp. 215–217 (1999)
Solberg, A.H.S., Dokken, S.T., Solberg, R.: Automatic detection of oil spills in envisat, radarsat and ers sar images. In: IEEE International Geoscience and Remote Sensing Symposium, vol. 4, pp. 2747–2749 (2003)
Liu, A., Peng, C., Chang, S.S.: Wavelet analysis of satellite image for coastal watch. IEEE International Journal of Oceanic Engineering 22, 9–17 (1997)
Jain, A., Zongker, D.: Feature selection: Evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 153–158 (1997)
Nirchio, F., Sorgente, M., Giancaspro, A., Biaminos, W., Parisato, E., Ravera, R., Trivero, P.: Automatic detection of oil spills from sar images. International Journal of Remote Sensing 26, 1157–1174 (2005)
Bevk, M., Kononenko, I.: A statistical approach to texture description of medical images: a preliminary study. In: Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems, pp. 239–244 (2002)
Chang, T., Kuo, C.C.J.: A wavelet transform approach to texture analysis. IEEE Transactions on Image Processing 4, 429–441 (1992)
Haralick, R.M., Shapiro, L.G.: Computer and robot vision. Addison-Wesley, New York (1992)
Livens, S.: Image Analysis for Material Characterization. PhD thesis, Universiteit Antwerpen (1998)
Castellano, G., Bonilha, L., Li, L.M., Cendes, F.: Texture analysis of medical images. Clinical Radiology 59, 1061–1069 (2004)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3, 610–621 (1973)
de Wouwer, G.V.: Wavelets for Multiscale Texture Analysis. PhD thesis, Universiteit Antwerpen (1998)
Richards, J., Jia, X.: Remote Sensing Digital Image Analysis - An Introduction. Springer, Heidelberg (1999)
Webb, A.R.: Statistical Pattern Recognition, 2nd edn. Wiley, England (2002)
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de A. Lopes, D.F., Ramalho, G.L.B., de Medeiros, F.N.S., Costa, R.C.S., Araújo, R.T.S. (2006). Combining Features to Improve Oil Spill Classification in SAR Images. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_103
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DOI: https://doi.org/10.1007/11815921_103
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