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Multi-objective Entropy Evolutionary Algorithm for Marine Oil Spill Detection Using Cosmo-skymed Satellite Data : Volume 12, Issue 3 (25/06/2015)

By Marghany, M.

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Book Id: WPLBN0004020858
Format Type: PDF Article :
File Size: Pages 27
Reproduction Date: 2015

Title: Multi-objective Entropy Evolutionary Algorithm for Marine Oil Spill Detection Using Cosmo-skymed Satellite Data : Volume 12, Issue 3 (25/06/2015)  
Author: Marghany, M.
Volume: Vol. 12, Issue 3
Language: English
Subject: Science, Ocean, Science
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2015
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Marghany, M. (2015). Multi-objective Entropy Evolutionary Algorithm for Marine Oil Spill Detection Using Cosmo-skymed Satellite Data : Volume 12, Issue 3 (25/06/2015). Retrieved from http://worldlibrary.net/


Description
Description: Geoscience {&} Digital Earth Center, Research Institute for Sustainability & Environment, Universiti Teknologi Malaysia, 81310 Skudai, UTM, Johor, Malaysia. Oil spill pollution has a substantial role in damaging the marine ecosystem. Oil spill that floats on top of water, as well as decreasing the fauna populations, affects the food chain in the ecosystem. In fact, oil spill is reducing the sunlight penetrates the water, limiting the photosynthesis of marine plants and phytoplankton. Moreover, marine mammals for instance, disclosed to oil spills their insulating capacities are reduced, and so making them more vulnerable to temperature variations and much less buoyant in the seawater. This study has demonstrated a design tool for oil spill detection in SAR satellite data using optimization of Entropy based Multi-Objective Evolutionary Algorithm (E-MMGA) which based on Pareto optimal solutions. The study also shows that optimization entropy based Multi-Objective Evolutionary Algorithm provides an accurate pattern of oil slick in SAR data. This shown by 85 % for oil spill, 10 % look-alike and 5 % for sea roughness using the receiver-operational characteristics (ROC) curve. The E-MMGA also shows excellent performance in SAR data. In conclusion, E-MMGA can be used as optimization for entropy to perform an automatic detection of oil spill in SAR satellite data.

Summary
Multi-objective entropy evolutionary algorithm for marine oil spill detection using cosmo-skymed satellite data

Excerpt
Amorocho, J. and Espildora, B.: Entropy in the assessment of uncertainty in hydrologic systems and models, Water Resour. Res., 9, 1511–1522, 1973.; Barni, M., Betti, M., and Mecocci, A.: A fuzzy approach to oil spill detection an SAR images, International Geoscience and Remote Sensing Symposium, 1995, IGARSS'95, Quantitative Remote Sensing for Science and Applications, 157–159, 1995.; Calabresi, G., Del Frate, F., Lichtenegger, J., Petrocchi, A., and Trivero, P.: Neural networks for the oil spill detection using ERS-SAR data, IEEE 1999 International Geoscience and Remote Sensing Symposium, 1999, IGARSS'99 Proceedings, 215–217, 1999.; Chapman, T. G.: Entropy as a measure of hydrologic data uncertainty and model performance, J. Hydrol., 85, 111–126, 1986.; Cloude, S. R. and Pottier, E.: A review of target decomposition theorems in radar polarimetry, IEEE T. Geosci. Remote, 34, 498–518, 1996.; Coello, C. A. C., Van Veldhuizen, D. A., and Lamont, G. B.: Evolutionary Algorithms for Solving Multi-Objective Problems, Springer, 2002.; Fukunaga, K.: Introduction to Statistical Pattern Recognition, Academic Press, 2013.; Garcia-Pineda, O., MacDonald, I. R., Li, X., Jackson, C. R., and Pichel, W. G.: Oil spill mapping and measurement in the Gulf of Mexico with Textural Classifier Neural Network Algorithm (TCNNA), IEEE J. Sel. Top. Appl., 6, 2517–2525, 2013.; Gunawan, S., Farhang-Mehr, A., and Azarm, S.: On maximizing solution diversity in a multiobjective multidisciplinary genetic algorithm for design optimization, 2004.; Harmancioglu, N.: Measuring the information content of hydrological processes by the entropy concept, Centennial of Ataturk's Birth, Journal of the Civil Engineering Faculty of Ege University, 12, 13–88, 1981.; Lathi, B. P.: An introduction to random signals and communication theory, 1968.; Liu, P., Li, X., Qu, J. J., Wang, W., Zhao, C., and Pichel, W.: Oil spill detection with fully polarimetric UAVSAR data, Mar. Pollut. Bull., 62, 2611–2618, 2011.; Lombardini, P., Fiscella, B., Trivero, P., Cappa, C., and Garrett, W.: Modulation of the spectra of short gravity waves by sea surface films: slick detection and characterization with a microwave probe, J. Atmos. Ocean. Tech., 6, 882–890, 1989.; Marghany, M.: Utilization of a genetic algorithm for the automatic detection of oil spill from RADARSAT-2 SAR satellite data, Mar. Pollut. Bull., 89, 20–29, 2014a.; Marghany, M.: Multi-objective evolutionary algorithm for oil spill detection from COSMO-SkeyMed satellite, in: Computational Science and Its Applications–ICCSA 2014, Springer, 355–371, 2014b.; Marghany, M. and van Genderen, J.: Entropy algorithm for automatic detection of oil spill from radarsat-2 SAR data, IOP Conference Series: Earth and Environmental Science, 012051, 2014.; Minchew, B., Jones, C. E., and Holt, B.: Polarimetric analysis of backscatter from the Deepwater Horizon oil spill using L-band synthetic aperture radar, IEEE T. Geosci. Remote, 50, 3812–3830, 2012.; Mohanta, R. K. and Sethi, B.: A review of genetic algorithm application for image segmentation, International Journal of Computer Technology & Applications, 3, 720–723, 2012.; Nirchio, F., Sorgente, M., Giancaspro, A., Biamino, W., Parisato, E., Ravera, R., and Trivero, P.: Automatic detection of oil spills from SAR images, Int. J. Remote Sens., 26, 1157–1174, 2005.; Shi, L., Zhao, C., Fan, K., Shi, Y., and Liu, P.: Texture feature application in oil spill detection by satellite data, Congress on Image and Signal Processing, 2008, CISP'08, 784–788, 2008.; Skrunes, S., Brekke, C., and Eltoft, T.: An experimental study on oil spill characterization by multi-polarization SAR, 9th European Conference on Synthetic Aperture Radar, 2012, EUSAR, 139–142, 2012.; Staples, G. and Rodrigues, D. F.: Maritime environmental surveillance with RADARSAT-2, Anais XVI Simpósio Brasileiro de Sensoriamento Remoto – SBSR, Foz do Iguaçu, PR, Brasil, 13–18 april 2013, INPE, available at:

 

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