PARAMETER TUNING OF UNSUPERVISED ALGORITHMS TO IDENTIFY OIL SPILLS ON THE SEA SURFACE

Oil spills frequently occur on the sea surface due to heightened vessel activities. Oil spills can be detected by applying supervised and unsupervised classification methods to satellite images using radar sensors. Supervised classification methods such as visual interpretation are widely used, but the results are very subjective. Conversely, unsupervised methods, while less subjective, necessitate parameter tuning for accurate results. This study's primary goal is to assess the impact of parameter tuning on unsupervised K-Means and Clustering Large Applications (CLARA) algorithms for detecting sea surface oil spills. It can be concluded that the area of identified oil spills is closely related to the iteration parameters and the number of cluster centers. The results of identification using the unsupervised method with these two algorithms will be compared with reference data from Indonesia National Institute of Aeronautics and Space (LAPAN) as the official institution that provides information regarding oil spills pollution on the sea surface in Indonesia. The main conclusion from this study, parameter tuning is highly required before carrying out the process of identifying oil spills on sea level using the unsupervised method especially related to the number of iterations executed, the desired number of cluster centers, and the clustering type of the algorithm used. Using the tuned parameters, the K-Means algorithm is able to identify oil spill areas that are quantitatively close to the reference data area, but the CLARA algorithm is able to provide identification results that have fewer errors in terms of oil spills look-alikes. © 2024 SPIE.