OIL SPILL DETECTION AND CHARACTERIZATION USING FULLY-POLARIMETRIC X AND C BAND SAR IMAGERY: A NEAR REAL TIME PERSPECTIVE

摘要

We explores the possibilities and advantages of quad polarimetric SAR data for the purpose of oil spill detection and discrimination of different types of slicks and lookalikes. An array of polarimetric features derived from the Pauli and lexicographic basis scattering matrices have been proposed. Those sets of features are then used to train and validate an Artificial Neural Network classifier. On a dataset of near-coincident TerraSAR-X (TS-X) and RADARSAT-2 (RS-2) acquisitions, we perform a feature analysis in terms of relevance and redundancy for oil slick characterization and ranked according to their ability to discriminate between oil spills and look-alikes. Polarimetric features such as Scattering diversity, Surface scattering fraction, Entropy and Span proved to be more discriminative than other polarimetric features.