Investigations into the X and C band Quad-Pol features for oil slick characterization

Abstract:
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 look-alikes. An array of polarimetric features derived from the Pauli and lexicographic basis scattering matrices, both in C and X-band have been proposed. Those sets of features are then used to train and validate an ANN 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.