Cross-domain　ship　detection　tries　to　identify　synthetic　aperture　radar　(SAR)　ships　by　adapting　knowledge　from　labeled　optical　images,　without　labor-intensive　annotations.　In　practical　applications,　a　few　(e.g.,　one　or　three　samples)　labeled　SAR　samples　are　available,　which　provides　additional　supervision　for　SAR　ships.　However,　the　existing　cross-domain　methods　ignore　the　SAR　supervision　(a　few　labeled　and　unlabeled　SAR　images),　which　limits　their　performances　in　a　practical　and　under-investigated　task:　semisupervised　cross-domain　ship　detection　(SCSD).　In　this　article,　a　dual-teacher　framework　is　proposed　to　address　the　mutual　interference　between　optical　supervision　and　SAR　supervision.　First,　both　optical　and　SAR　supervision　are　decomposed　into　two　subtasks:　cross-domain　task　and　semisupervised　task.　Then,　both　cross-domain　tasks　and　semisupervised　tasks　can　be　learned　interactively　in　two　individual　teacher-student　models.　The　teacher-student　models　generate　pseudo-labels　on　unlabeled　SAR　images　by　a　teacher　network　and　fine-tune　the　student　network.　Finally,　the　dual-teacher　framework　retrains　two　teacher-student　models　in　cotraining　strategies.　Both　cross-domain　datasets　and　semisupervised　datasets　are　exploited　to　jointly　improve　the　pseudo-label　quality.　The　effectiveness　of　the　dual-teacher　framework　has　been　fully　experimentally　demonstrated.　The　code　is　available　at　https://github.com/XiangtaoZheng/DualTeacher.