Abstract
This chapter demonstrates a design tool for oil spill detection in synthetic aperture radar (SAR) satellite data using optimization of Entropy-based Multiobjective Evolutionary Algorithm (E-MOEA) and Nondominated Sorting Genetic Algorithm-II (NSGA-II), which is based on Pareto-optimal solutions. The study also shows that optimization of NSGA-II provides an accurate pattern of the oil slick in SAR data compared with E-MOEA. This has a standard error of 0.04 and less running time of 65s. The NSGA-II can also automatically identify a thick oil slick from thin and light ones. In conclusion, E-MOEA can be used as an optimization tool for entropy to perform automatic detection of oil spills in SAR satellite data.
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