Empirical Analysis of Oil Spill Detection Methods
Date Issued
January 1, 2024
DOI
10.1109/IGARSS53475.2024.10642731
Abstract
Oil spills are a major source of marine pollution affecting the environment, economy, and marine ecosystems. Toxic chemicals from oil spills can remain in the ocean for years and even sink to the seabed, affecting sedimentation rates. Although many oil spills are caused by accident, some are caused intentionally by cargo ships dumping waste oil and bilge water. It is very difficult to locate, detect and remove oil from the ocean surface. However, regular monitoring can help prevent illegal dumping and aid remediation efforts. This work aims to detect oil spills in the North-Eastern part of Cyprus using a deep learning model. The results are compared with a conventional Adaptive Thresholding Algorithm. The comparisons demonstrate that the deep learning model has higher accuracy than the adaptive thresholding algorithm.