In　this　paper,　we　present　a　comparison　between　several　algorithms　for　oil　spill　classifications　using　fully　and　compact　polarimetric　SAR　images.　Oil　spill　is　considered　as　one　of　the　most　significant　sources　of　marine　pollution.　As　a　major　difficulty　of　SAR-based　oil　spill　detection　algorithms　is　the　classification　between　mineral　and　biogenic　oil,　we　focus　on　quantitatively　analyzing　and　comparing　fully　and　compact　polarimetric　satellite　synthetic　aperture　radar　(SAR)　modes　to　detect　hydrocarbon　slicks　over　the　sea　surface,　discriminating　them　from　weak-damping　surfactants,　such　as　biogenic　slicks.　The　experiment　was　conducted　on　quad-pol　SAR　data　acquired　during　the　Norwegian　oil-on-water　experiment　in　2011.　A　universal　procedure　was　used　to　extract　the　features　from　quad-,　dual-　and　compact　polarimetric　SAR　modes　to　rank　different　polarimetric　SAR　modes　and　common　supervised　classifiers.　Among　all　the　dual-　and　compact　polarimetric　SAR　modes,　the　/2　mode　has　the　best　performance.　The　best　supervised　classifiers　vary　and　depended　on　whether　sufficient　polarimetric　information　can　be　obtained　in　each　polarimetric　mode.　We　also　analyzed　the　influence　of　the　number　of　polarimetric　parameters　considered　as　inputs　for　the　supervised　classifiers,　onto　the　detection/discrimination　performance.　We　discovered　that　a　feature　set　with　four　features　is　sufficient　for　most　polarimetric　feature-based　oil　spill　classifications.　Moreover,　dimension　reduction　algorithms,　including　principle　component　analysis　(PCA)　and　the　local　linear　embedding　(LLE)　algorithm,　were　employed　to　learn　low　dimensional　and　distinctive　information　from　quad-polarimetric　SAR　features.　The　performance　of　the　new　feature　sets　has　comparable　performance　in　oil　spill　classification.