The　growing　availability　of　high-quality　remote　sensing　imagery　has　led　to　increased　interest　in　semantic　change　detection　(SCD).　Supervised　methods　for　this　task　have　shown　significant　performance　improvements,　but　acquiring　labeled　data　is　often　challenging　and　expensive.　To　confront　this　challenge,　we　propose　a　semisupervised　approach　for　SCD　in　remote　sensing　images　using　an　innovative　teacher-student　model.　We　use　a　convolutional　neural　network　(CNN)　in　the　teacher　model　and　a　fusion　design　combining　CNN　and　vision　transformer　in　the　student　model,　with　the　rationale　that　the　CNN　requires　fewer　training　samples　compared　with　vision　transformer,　and　fusion　network　allows　us　to　leverage　the　advantages　of　both.　To　further　enhance　the　model＇s　performance,　we　propose　a　novel　data　augmentation　approach　by　interchanging　bitemporal　images　as　well　as　their　labels.　The　principle　for　that　is　the　change　from　one　moment　to　another　and　vice　versa　are　two　different　changes　and　can,　therefore,　be　used　to　augment　the　training　dataset.　More　importantly,　this　method　does　not　reduce　its　reliability,　because　no　noise　is　brought　to　the　remote　sensing　images.　By　adopting　this　approach,　we　are　able　to　better　utilize　the　small　labeled　dataset　to　increase　the　precision　of　the　model　while　maintaining　the　robustness.　According　to　the　experimental　results,　the　proposed　method　outperforms　several　state-of-the-art　methods　and　achieves　an　improvement　compared　with　bi-temporal　semantic　reasoning　network　(Bi-SRNet)　in　mean　intersection　over　union　(mIoU)/separated　kappa　(SeK)/overall　accuracy　(OA)　of　3.25/4.42/1.78,　6.37/11.72/2.91　on　the　SECOND　and　Landsat-SCD　datasets,　respectively.