Oceans are considered as the important source for oil reserves and continuous activities like oil extraction and transportation may sometimes cause the accidental release of oil into the sea surface which causes a major threat to the marine ecosystems, economy and human life. The prime focus of this study is to explore the potential of the fully polarimetric SAR data and analyze the different scattering mechanisms for the oil spilled regions. In this study the fully polarized and orthorectified, L band data of UAVSAR airborne sensor is used which is captured on June 22nd 2010, during which the Deepwater Horizon oil spill occurred in the Gulf of Mexico. For the detection of oil spill different decomposition techniques such as Freeman, Yamaguchi and H/A/α are studied and classified using Wishart classification. Freeman and Yamaguchi decomposition helped in understanding the type of scattering mechanism taking place in slick covered regions, sea surface and in the presence of ships/rig. A set of polarimetric parameters such as magnitude of correlation coefficient, cross product of co polarized channels, anisotropy, alpha ,entropy and the intensity of the coherency matrix are studied which helped in distinguishing the oil spills, sea surface and the look-alikes. The Wishart classification result of Freeman and Yamaguchi decompositions showed more reliable results in comparison to the K-means classification results obtained through segmentation of combined H/A/α decomposition. The entropy, anisotropy and magnitude of correlation coefficient are dependent on the angle of incidence. At low incidence angle the entropy value of oil spills are similar to that of the sea surface whereas the magnitude of correlation coefficient which is a function of dielectric constant, increases for oil spills at low incidence angle. The polarimetric parameter, intensity of the coherency matrix utilizes the whole coherency matrix by calculating its determinant and proven to provide good discrimination between the oil spills and the sea surface.