To　scientifically　plan　and　accurately　manage　the　coastal　aquaculture　industry,　it　is　especially　critical　to　quickly　and　accurately　extract　raft　aquaculture　areas.　In　the　study,　the　Raft-Former　was　designed　to　accurately　extract　coastal　raft　aquaculture　in　Sansha　Bay　using　Sentinel-2　remote　sensing　imagery.　Specifically,　a　Feature　Enhancement　Module　(FEM)　was　designed　to　selectively　learn　the　interest　features　for　solving　the　omission　and　mis-extraction　caused　by　changes　in　the　coastal　environment.　For　the　boundary　adhesion　problems　caused　by　the　dense　distribution　of　raft　aquaculture　areas,　a　Feature　Alignment　Module　(FAM)　was　developed　to　enhance　edge-aware　ability.　A　Global-Local　Fusion　Module　(GLFM)　was　introduced　to　effectively　integrate　the　local　features　with　multi-scale　and　global　features　to　overcome　significant　scale　differences　in　aquaculture　areas.　Numerous　experiments　show　that　our　method　is　better　than　the　state-of-the-art　models.　Specifically,　Raft-Former　respectively　achieves　90.05%　and　86.73%　mIoU　on　the　Sansha　Bay　dataset.