Abstract
The aim of this study is to propose and test a new methodology for detection of oil spills in the world oceans from advanced synthetic aperture radar imagery embedded in ENVISAT satellite (ENVISAT-ASAR). The proposed and applied methodology includes four levels: data acquisition, dark spots detection, features extraction and dark spots classification for discrimination between oil spills and look-alikes. Level 1 contains the ENVISAT-ASAR wide swath mode data acquisition. Level 2 begins with a visual interpretation based on experience and a priori information concerning location, external information about weather conditions, differences in shape, and contrast to surroundings between oil spills and look-alikes, then filtering and segmentation. Level 3 contains extraction of features from the detected dark spots. Level 4 aim is to discriminate oil spills from look-alikes using the features extracted by means of object-based fuzzy classification. As a result, oil slicks are discriminated from look-alikes with an overall accuracy classification of 91% for oil slicks and 86% for look-alikes. Finally, to validate our results, the method has been tested by comparing the areas of the automatically detected oil spills (object-based fuzzy classification) with the areas of the manually detected oil spills (region of interest), by means of area ratios.
Published Version
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