As　a　major　marine　pollution　source,　oil　spills　largely　threaten　the　sustainability　of　the　coastal　environment.　Polarimetric　synthetic　aperture　radar　remote　sensing　has　become　a　promising　approach　for　marine　oil　spill　detection　since　it　could　effectively　separate　crude　oil　and　biogenic　look-alikes.　However,　on　the　sea　surface,　the　signal　to　noise　ratio　of　Synthetic　Aperture　Radar　(SAR)　backscatter　is　usually　low,　especially　for　cross-polarized　channels.　In　practice,　it　is　necessary　to　combine　the　refined　detail　of　slick-sea　boundary　derived　from　the　co-polarized　channel　and　the　highly　accurate　crude　slick/look-alike　classification　result　obtained　based　on　the　polarimetric　information.　In　this　paper,　the　architecture　for　oil　spill　detection　based　on　polarimetric　SAR　is　proposed　and　analyzed.　The　performance　of　different　polarimetric　SAR　filters　for　oil　spill　classification　are　compared.　Polarimetric　SAR　features　are　extracted　and　taken　as　the　input　of　Staked　Auto　Encoder　(SAE)　to　achieve　high　accurate　classification　between　crude　oil,　biogenic　slicks,　and　clean　sea　surface.　A　post-processing　method　is　proposed　to　combine　the　classification　result　derived　from　SAE　and　the　refined　boundary　derived　from　VV　channel　power　image　based　on　special　density　thresholding　(SDT).　Experiments　were　conducted　on　spaceborne　fully　polarimetric　SAR　images　where　both　crude　oil　and　biogenic　slicks　were　presented　on　the　sea　surface.