Anais do XIX Simpósio Brasileiro de Sensoriamento Remoto

Our research refines the ability to discriminate the sea surface expression of naturally-occurring oil seeps and man-made oil spills. A RADARSAT-2 dataset (244) is analyzed to investigate oil slicks (4,562) observed in the Gulf of Mexico (Campeche Bay, Mexico). As the scientific literature on the use of satellite-derived measurements to discriminate the oil-slick category is sparse, our research addresses this gap by extending earlier investigations aimed at discriminating seeps from spills. To reveal hidden traits of the available satellite information and to evaluate an existing Oil-Slick Discrimination Algorithm, distinct Data Processing Segments methodically inspect the data at several levels. Our Exploratory Data Analysis discloses the Log10 normalization with the most effective discrimination power of about 70%. Our refined analysis corroborates and consolidates our earlier findings, providing a firmer basis and useful accuracies of seep-spill discrimination using space-borne Synthetic Aperture Radars information.