The　recognition　of　sea　ice　is　of　great　significance　for　reflecting　climate　change　and　ensuring　the　safety　of　ship　navigation.　Recently,　many　deep　learning　based　methods　have　been　proposed　and　applied　to　segment　and　recognize　sea　ice　regions.　However,　the　diverse　scales　of　sea　ice　areas,　the　zigzag　and　fine　edge　contours,　and　the　difficulty　in　distinguishing　different　types　of　sea　ice　pose　challenges　to　existing　sea　ice　recognition　models.　In　this　paper,　a　Global-Local　Detail　Guided　Transformer　(GDGT)　method　is　proposed　for　sea　ice　recognition　in　optical　remote　sensing　images.　In　GDGT,　a　global-local　feature　fusiont　mechanism　is　designed　to　fuse　global　structural　correlation　features　and　local　spatial　detail　features.　Furthermore,　a　detail-guided　decoder　is　developed　to　retain　more　high-resolution　detail　information　during　feature　reconstruction　for　improving　the　performance　of　sea　ice　recognition.　Experiments　on　the　produced　sea　ice　dataset　demonstrated　the　effectiveness　and　advancement　of　GDGT.　©　2024　IEEE.