ASSESSMENT OF POLARIMETRIC VARIABILITY BY DISTANCE GEOMETRY FOR ENHANCED CLASSIFICATION OF OIL SLICKS USING SAR

In this paper, we introduce a new approach for investigation of polarimetric Synthetic Aperture Radar (PolSAR) images for oil slick analysis. Our method aims at enhancing discrimination of oil types by exploring the polarimetric features that can be produced by processing PolSAR scenes without dimensionality reduction. Taking advantage of a mixture description of the interactions among classes within the dataset and a characterization of their intra- and inter-class variability, our algorithm is able to quantify the areal coverage of different elements. These estimates can be used to hence improve classification. Experimental results on a PolSAR dataset acquired by unmanned aerial vehicle (UAV) on oil slicks in open water show the capacity of our method.