Optimal Detection and Estimation of Marine Oil Spills Through Coherent Pluralism

An important area of environmental science involves the combination of information from diverse sources relating to a similar endpoint. Majority of optical remote sensing techniques used for marine oil spills detection have been reported lately of having high number of false alarms (oil slick look-a-likes) phenomena which give rise to signals which appear to be oil but are not. Suggestions for radar image as an operational tool has also been made. However, due to the inherent risk in these tools, this paper presents the possible research directions of combining statistical techniques with remote sensing in marine oil spill detection and estimation.

[1]  John Mingers,et al.  Variety is the spice of life: combining soft and hard OR/MS methods , 2000 .

[2]  Bryan Christiansen,et al.  Cultural Variations and Business Performance: Contemporary Globalism , 2012 .

[3]  E. Turkina.  Cross-Border Inter-Firm Networks in Contemporary Europe: The Effects of Structural and Cultural Embeddedness on Firm Performance , 2012 .

[4]  Rafael Diaz,et al.  A Simulation-Based Optimization Approach to a Lost Sale Stochastic Inventory Model , 2012, Int. J. Oper. Res. Inf. Syst..

[5]  K. Topouzelis,et al.  Detection and discrimination between oil spills and look-alike phenomena through neural networks , 2007 .

[6]  P. Pavlakis,et al.  An object‐oriented methodology to detect oil spills , 2006 .

[7]  G. Glass.  Primary, Secondary, and Meta-Analysis of Research1 , 1976 .

[8]  Mohamed Hasnain Isa,et al.  Effect of Initial Oil Concentration and Dispersant on Crude Oil Biodegradation in Contaminated Seawater , 2010, Bulletin of environmental contamination and toxicology.

[9]  D. Spiegelhalter,et al.  Modelling Complexity: Applications of Gibbs Sampling in Medicine , 1993 .

[10]  Peter Bennett,et al.  Uncertainty and conflict: Combining conflict analysis and strategic choice , 1990 .

[11]  Chris T. Kiranoudis,et al.  Automatic identification of oil spills on satellite images , 2006, Environ. Model. Softw..

[12]  Fabio Del Frate,et al.  Neural networks for oil spill detection using ERS-SAR data , 2000, IEEE Trans. Geosci. Remote. Sens..

[13]  Michael Jackson.  Towards coherent pluralism in management science , 1999, J. Oper. Res. Soc..

[14]  G. Larry Bretthorst,et al.  Physics and Probability: On the Difference in Means , 1993 .

[15]  G. Midgley.  Pluralism and the legitimation of systems science , 1992 .

[16]  R. Simon,et al.  Controlling the number of false discoveries: application to high-dimensional genomic data , 2004 .

[17]  P. Dunscombe,et al.  A Monte Carlo simulation approach to the characterization of uncertainties in cancer staging and radiation treatment decisions , 2007, J. Oper. Res. Soc..

[18]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  D. S. Etkin,et al.  Estimating Cleanup Costs for Oil Spills , 1999 .

[20]  Pavlo Tkalich,et al.  A multiphase oil spill model , 2003 .

[21]  Walter W. Piegorsch,et al.  Combining environmental information. II: Environmental epidemiology and toxicology , 1996 .

[22]  Ue-Pyng Wen,et al.  Equal Pricing Strategies in a Dual Channel Supply Chain , 2011, Int. J. Oper. Res. Inf. Syst..

[23]  John Mingers,et al.  Multimethodology: Towards a framework for mixing methodologies , 1997 .

[24]  Fátima N. S. de Medeiros,et al.  Using Boosting to Improve Oil Spill Detection in SAR Images , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[25]  Philip C. Gregory,et al.  Bayesian Logical Data Analysis for the Physical Sciences: Acknowledgements , 2005 .

[26]  Konstantinos N. Topouzelis,et al.  Oil Spill Detection by SAR Images: Dark Formation Detection, Feature Extraction and Classification Algorithms , 2008, Sensors.

[27]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[28]  Xuegang Ban,et al.  A Link-Node Nonlinear Complementarity Model for a Multiclass Simultaneous Transportation Dynamic User Equilibria , 2013, Int. J. Oper. Res. Inf. Syst..

[29]  Incorporating parameter uncertainty into prediction intervals for spatial data modeled via a parametric variogram , 2003 .