In　the　semantic　segmentation　of　high-resolution　remote　sensing　images,　utilizing　the　normalized　Digital　Surface　Model　(nDSM)　that　provides　height　information　as　auxiliary　data　and　fusing　it　with　the　visible　image　can　improve　the　accuracy　of　segmentation.　However,　the　better　utilization　of　complementarity　between　different　modal　features　has　not　been　fully　explored.　In　this　work,　we　propose　a　new　dual-branch　and　multi-stage　Bimodal　Fusion　Rectification　Network　(BFRNet),　which　is　end-to-end　trainable.　It　consists　of　three　modules:　Channel　and　Spatial　Fusion　Rectification　(CSFR)　module,　Edge　Fusion　Refinement　(EFR)　module,　and　Multiscale　Feature　Fusion　(MSFF)　module.　The　CSFR　module　integrates　and　rectifies　multimodal　features　in　both　channel　and　spatial　dimensions,　achieving　sufficient　interaction　and　fusion　between　multimodal　features.　The　EFR　module　obtains　better　multiscale　edge　features　than　single　modality　through　feature　fusion　based　on　bimodal　interactive　edge　attention　and　spatial　gate,　which　helps　to　alleviate　the　edge　loss　of　ground　objects　in　single　modality.　The　MSFF　module　is　used　to　upsample　and　fuse　multiscale　features　from　EFR　and　CSFR　to　generate　the　final　semantic　segmentation　results.　The　experimental　results　on　the　two　public　datasets,　Vaihingen　and　Potsdam,　provided　by　ISPRS,　showcase　the　comparative　advantage　of　the　proposed　method　over　other　research　methods.