Semantic　change　detection　(SCD)　in　high-resolution　(HR)　remote　sensing　images　faces　two　issues:　(1)　isolated　network　branch　for　binary　change　detection　(BCD)　within　multi-task　architecture　result　in　suboptimal　SCD　performance;　(2)　false　alarms　or　missed　detections　caused　by　illumination　differences　or　seasonal　transform.　To　address　these　issues,　this　study　proposes　a　bi-temporal　binary　change　enhancement　network　(Bi-BCENet).　Specifically,　we　introduce　a　binary　change　enhancement　(BCE)　strategy　based　on　multi-network　joint　learning　to　achieve　superior　SCD　via　improving　change　areas　prediction.　Within　the　network’s　reasoning　process,　we　develop　a　cross-attention　fusion　module　(CAFM)　to　enhance　the　global　similarity　modeling　via　cross-network　prompt　fusion,　and　we　employ　a　cosine　similarity-based　auxiliary　loss　to　optimize　non-change’s　semantic　consistency.　The　experiments　on　SECOND　and　CINA-FX　datasets　demonstrate　that　Bi-BCENet　outperforms　representative　SCD　networks,　achieving　62.08%,　84.95%　in　FSCD　and　66.88%,　83.10%　in　mIoUsCD,　respectively.　And　the　ablation　analysis　of　network　validates　Bi-BCENet’s　effectiveness　in　reducing　false　alarms　and　missed　detections　in　SCD　results.　Moreover,　for　specific　SCD　of　cropland,　Bi-BCENet　shows　its　strong　potential　in　single-to-multi　SCD.　©　2004-2012　IEEE.