Metaheuristic Search Algorithms for Oil Spill Detection Using SAR Images

In this research, we provide an image processing methodology based meta-heuristic search algorithm to perform segmentation-based clustering on Synthetic Aperture Radar (SAR) oil spill images. The proposed process will help to detect oil spills using SAR images and estimate the amount of oil spilled in a region. A sample image is evaluated using three different meta-heuristic search algorithms including Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to determine the oil spill region; the results of the three algorithms are then compared. The three algorithms are used to determine the optimal cluster centers for three clusters (water, oil, and a mix of water and oil). The main advantage of this proposed method is its accuracy in determining the optimal cluster centers, which enhances oil spill detection in an area.