Polarimetric　synthetic　aperture　radar　(SAR)　remote　sensing　provides　an　outstanding　tool　in　oil　spill　detection　and　classification,　for　its　advantages　in　distinguishing　mineral　oil　and　biogenic　lookalikes.　Various　features　can　be　extracted　from　polarimetric　SAR　data.　The　large　number　and　correlated　nature　of　polarimetric　SAR　features　make　the　selection　and　optimization　of　these　features　impact　on　the　performance　of　oil　spill　classification　algorithms.　In　this　paper,　deep　learning　algorithms　such　as　the　stacked　autoencoder　(SAE)　and　deep　belief　network　(DBN)　are　applied　to　optimize　the　polarimetric　feature　sets　and　reduce　the　feature　dimension　through　layer-wise　unsupervised　pre-training.　An　experiment　was　conducted　on　RADARSAT-2　quad-polarimetric　SAR　image　acquired　during　the　Norwegian　oil-on-water　exercise　of　2011,　in　which　verified　mineral,　emulsions,　and　biogenic　slicks　were　analyzed.　The　results　show　that　oil　spill　classification　achieved　by　deep　networks　outperformed　both　support　vector　machine　(SVM)　and　traditional　artificial　neural　networks　(ANN)　with　similar　parameter　settings,　especially　when　the　number　of　training　data　samples　is　limited.