With　the　deepening　research　and　cross-fusion　in　the　modern　remote　sensing　image　area,　the　classification　of　high　spatial　resolution　remote　sensing　images　has　captured　the　attention　of　the　researchers　in　the　field　of　remote　sensing.　However,　due　to　the　serious　phenomenon　of　same　object,　different　spectrum　and　same　spectrum,　different　object　of　high-resolution　remote　sensing　image,　the　traditional　classification　strategy　is　hard　to　handle　this　challenge.　In　this　paper,　a　remote　sensing　image　scene　classification　model　based　on　SENet　and　Inception-V3　is　proposed　by　utilizing　the　deep　learning　method　and　transfer　learning　strategy.　The　model　first　adds　a　dropout　layer　before　the　full　connection　layer　of　the　original　Inception-V3　model　to　avoid　over-fitting.　Then　we　embed　the　SENet　module　into　the　Inception-V3　model　for　optimizing　the　network　performance.　In　this　paper,　global　average　pooling　is　used　as　squeeze　operation,　and　then　two　fully　connected　layers　are　used　to　construct　a　bottleneck　structure.　The　model　proposed　in　this　paper　is　more　non-linear,　can　better　fit　the　complex　correlation　between　channels,　and　greatly　reduces　the　amount　of　parameters　and　computation.　In　the　training　process,　this　paper　adopts　the　transfer　learning　strategy,　makes　full　use　of　existing　models　and　knowledge,　improves　training　efficiency,　and　finally　obtains　scene　classification　results.　The　experimental　results　based　on　AID　high-score　remote　sensing　scene　images　show　that　SE-Inception　has　faster　convergence　speed　and　more　stable　training　effect　than　the　original　Inception-V3　training.　Compared　with　other　traditional　methods　and　deep　learning　networks,　the　improved　model　proposed　in　this　paper　has　greater　accuracy　improvement.　©　2020　Z.　L.　Cai　et　al.