Detection of Oil Spill and Look-Alike from SAR Imagery Based on Ontology and Kernel Fuzzy C-Means

Abstract

Oil spills is a major threat to ocean ecosystems. The capability of synthetic aperture radar (SAR) sensors to detect oil spills over the sea surface is established and proven. Oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena (e.g., manmade actions, geological conditions, and meteorological and hydrological effects). The current researches aims to distinguish oil spills or look-alikes. The methods include three main parts, dark formation detection, feature extraction, and classification. To further improve the accuracy and efficiency of SAR image segmentation in the detection of marine oil spill, this study presents a new framework for oil spill and look-alike classification based on kernel fuzzy C-means (KFCM) and ontology. Ontologies are employed to model semantic concepts about shape, existence time, forming reason, and texture. Pre-classification can be performed by matching each region with the concepts of ontology. Once the needed dark formations have been selected, KFCM algorithm is used for classification. Experiment results show that this method performs well.