Instance　segmentation　performance　in　remote　sensing　images　(RSIs)　is　significantly　affected　by　two　issues:　how　to　extract　accurate　boundaries　of　objects　from　remote　imaging　through　the　dynamic　atmosphere,　and　how　to　integrate　the　mutual　information　of　related　object　instances　scattered　over　a　vast　spatial　region.　In　this　study,　we　propose　a　novel　shape　guided　transformer　network　(SGTN)　to　accurately　extract　objects　at　the　instance　level.　Inspired　by　the　global　contextual　modeling　capacity　of　the　self-attention　mechanism,　we　propose　an　effective　transformer　encoder　termed　LSwin,　which　incorporates　vertical　and　horizontal　1-D　global　self-attention　mechanisms　to　obtain　better　global-perception　capacity　for　RSIs　than　the　popular　local-shifted-window　based　swin　transformer.　To　achieve　accurate　instance　mask　segmentation,　we　introduce　a　shape　guidance　module　(SGM)　to　emphasize　the　object　boundary　and　shape　information.　The　combination　of　SGM,　which　emphasizes　the　local　detail　information,　and　LSwin,　which　focuses　on　the　global　context　relationships,　achieve　excellent　RSI　instance　segmentation.　Their　effectiveness　was　validated　through　comprehensive　ablation　experiments.　Especially,　LSwin　is　proven　better　than　the　popular　ResNet　and　swin　transformer　encoders　at　the　same　level　of　efficiency.　Compared　to　other　instance　segmentation　methods,　our　SGTN　achieves　the　highest　average　precision　scores　on　two　single-class　public　datasets　(WHU　dataset　and　BITCC　dataset)　and　a　multiclass　public　dataset　(NWPU　VHR-10　dataset).