Marine　oil　spills　have　caused　serious　harm　to　the　costal　ecological　environment　and　marine　economy.　Synthetic　aperture　radar　(SAR)　has　become　a　major　equipment　for　oil　spill　detection　because　of　its　advantages　of　all-day　and　all-weather　observation　capability.　In　this　paper,　the　complex　convolutional　neural　network　(CVCNN)　framework　is　applied　for　marine　oil　spills　classification.　The　classification　performance　of　different　polarization　modes　on　marine　oil　spills　classification　is　analyzed.　Experimental　results　show　that　CP　SAR　modes　have　comparable　performance　as　QP　mode　for　marine　oil　spills　classification.　Among　them,　the　Circular　Transmit　and　Linear　Receive　(CTLR)　mode　has　the　best　classification　performance　in　the　framework　of　CVCNN　while　DP　(VVVH)　mode　is　another　promising　alternative　with　much　simpler　hardware　configuration　requirement.　©　2024　IEEE.