Oil　spill　detection　plays　an　important　role　in　marine　environment　protection.　Quad-polarimetric　Synthetic　Aperture　Radar　(SAR)　has　been　proved　to　have　great　potential　for　this　task,　and　different　SAR　polarimetric　features　have　the　advantages　to　recognize　oil　spill　areas　from　other　look-alikes.　In　this　paper　we　proposed　an　oil　spill　detection　method　based　on　convolutional　neural　network　(CNN)　and　Simple　Linear　Iterative　Clustering　(SLIC)　superpixel.　Experiments　were　conducted　on　three　Single　Look　Complex　(SLC)　quad-polarimetric　SAR　images　obtained　by　Radarsat-2　and　Spaceborne　Imaging　Radar-C/X-Band　Synthetic　Aperture　Radar　(SIR-C/X-SAR).　Several　groups　of　polarized　parameters,　including　H/A/Alpha　decomposition,　Single-Bounce　Eigenvalue　Relative　Difference　(SERD),　correlation　coefficients,　conformity　coefficients,　Freeman　3-component　decomposition,　Yamaguchi　4-component　decomposition　were　extracted　as　feature　sets.　Among　all　considered　polarimetric　features,　Yamaguchi　parameters　achieved　the　highest　performance　with　total　Mean　Intersection　over　Union　(MIoU)　of　90.5%.　It　is　proved　that　the　SLIC　superpixel　method　significantly　improved　the　oil　spill　classification　accuracy　on　all　the　polarimetric　feature　sets.　The　classification　accuracy　of　all　kinds　of　targets　types　were　improved,　and　the　largest　increase　on　mean　MIoU　of　all　features　sets　was　on　emulsions　by　21.9%.