Synthetic　aperture　radar　(SAR)　has　been　widely　applied　in　oil　spill　detection　on　the　sea　surface　due　to　the　advantages　of　wide　area　coverage,　all-weather　operation,　and　multi-polarization　characteristics.　Sentinel-1　satellites　can　provide　dual-polarized　SAR　data,　and　they　have　high　potential　for　successful　application　to　oil　spill　detection.　However,　the　characteristics　of　the　sea　surface　and　oil　film　on　different　images　are　not　the　same　when　imaging　at　different　locations　and　in　different　conditions,　which　leads　to　the　inconsistent　accuracy　of　these　images　with　the　application　of　the　current　oil　spill　detection　methods.　In　order　to　avoid　the　above　limitation,　we　propose　an　oil　spill　detection　method　using　image　stretching　based　on　superpixels　and　a　convolutional　neural　network.　Experiments　were　carried　out　on　eight　Sentinel-1　dual-pol　data,　and　the　optimal　superpixel　number　and　image　stretching　parameters　are　discussed.　Mean　intersection　over　union　(MIoU)　was　used　to　evaluate　classification　accuracy.　The　proposed　method　could　effectively　improve　the　classification　accuracy;　when　the　expansion　and　inhibition　coefficients　of　image　stretching　were　set　to　1.6　and　1.2　respectively,　the　experiments　achieved　a　maximum　MIoU　of　85.4%,　7.3%　higher　than　that　without　image　stretching.