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.
Clustering, Genetic Algorithm, Meta-heuristic, Oil spill, Particle swarm optimization, Simulated Annealing
Sheta, Alaa, Ajay Katangur, and Scott A. King. "Metaheuristic Search Algorithms for Oil Spill Detection Using SAR Images." In 2018 8th International Conference on Computer Science and Information Technology (CSIT), pp. 143-149. IEEE, 2018.
2018 8th International Conference on Computer Science and Information Technology, CSIT 2018