The　existing　studies　on　cross-domain　classification　of　remote　sensing　scenes　face　challenges　of　low　classification　accuracy　and　strong　dependence　on　source　domain　samples,　particularly　in　complex　urban　scenes.　In　this　paper,　we　proposed　an　unsupervised　multi-source　domain　adaptation　method　for　cross-domain　classification　of　high-resolution　remote　sensing　image　scenes.　Our　method　starts　from　extracting　source　domain　featuresusing　Swin　Transformer　as　the　backbone.　We　then　used　a　cross-domain　classification　loss　to　train　the　target　domain　model.　The　loss　consists　of　weighted　information　maximization　and　pseudo-labeling　loss.　The　weighted　information　maximization　loss　is　used　to　obtain　the　source　domain　feature　distributions　from　the　source　domain　model　and　combine　multiple　source　domain　models,　while　the　pseudo-labeling　loss　is　used　to　further　align　the　feature　distributions　of　the　target　and　source　domains.　Our　method　can　fully　utilize　the　existing　source　domain　samples　to　achieve　effective　classification　of　the　target　domain,　while　avoiding　over-reliance　on　source　domain　data.　We　conducted　multiple　cross-domain　classification　experiments　on　four　publicly　available　remote　sensing　scene　classification　datasets　(AID,　NWPU-　RESISC45,　U　C　Merced,　and　WHU-RS19)　as　well　as　the　Fuzhou　GF-2　dataset.　The　experimental　results　show　that　our　proposed　method　achieves　satisfactory　cross-domain　classification　results　for　remote　sensing　scenes　and　remains　applicable　in　complex　urban　scenes.　©　2023　IEEE.