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,　there　are　huge　differences　in　sea　ice　size　and　irregular　edge　profiles,　which　bring　challenges　to　the　existing　sea　ice　recognition.　In　this　article,　a　global-local　Transformer　network,　called　SeaIceNet,　is　proposed　for　sea　ice　recognition　in　optical　remote　sensing　images.　In　SeaIceNet,　a　dual　global-attention　head　(DGAH)　is　proposed　to　capture　global　information.　On　this　basis,　a　global-local　feature　fusion　(GLFF)　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.　Extensive　experiments　on　several　sea　ice　datasets　demonstrated　that　the　proposed　SeaIceNet　has　better　performance　than　the　existing　methods　in　multiple　evaluation　indicators.　Moreover,　it　excels　in　addressing　challenges　associated　with　sea　ice　recognition　in　optical　remote　sensing　images,　including　the　difficulty　in　accurately　identifying　irregular　frozen　ponds　in　complex　environments,　the　broken　and　unclear　boundaries　between　sea　and　thin　ice　that　hinder　precise　segmentation,　and　the　loss　of　high-resolution　spatial　details　during　model　learning　that　complicates　refinement.