Current　land　use　classification　models　based　on　very　high-resolution　(VHR)　remote　sensing　images　often　suffer　from　high　sample　dependence　and　poor　transferability.　To　address　these　challenges,　we　propose　an　unsupervised　multisource　domain　adaptation　framework　for　cross-domain　land　use　classification　that　eliminates　the　need　for　repeatedly　using　source　domain　data.　Our　method　uses　the　Swin　Transformer　as　the　backbone　of　the　source　domain　model　to　extract　features　from　multiple　source　domain　samples.　The　model　is　trained　on　source　domain　samples,　and　unlabeled　target　domain　samples　are　then　used　for　target　domain　model　training.　To　minimize　the　feature　discrepancies　between　the　source　and　target　domains,　we　use　a　weighted　information　maximization　loss　and　self-supervised　pseudolabels　to　alleviate　cross-domain　classification　noise.　We　conducted　experiments　on　four　public　scene　datasets　and　four　new　land　use　scene　datasets　created　from　different　VHR　images　in　four　Chinese　cities.　Results　show　that　our　method　outperformed　three　existing　single-source　cross-domain　methods　(i.e.,　DANN,　DeepCORAL,　and　DSAN)　and　four　multisource　cross-domain　methods　(i.e.,　M3SDA,　PTMDA,　MFSAN,　and　SHOT),　achieving　the　highest　average　classification　accuracy　and　strong　stability.　We　conclude　that　our　method　has　high　potential　for　practical　applications　in　cross-domain　land　use　classification　using　VHR　images.