The　scene　classification　of　a　remote　sensing　image　has　been　widely　used　in　various　fields　as　an　important　task　of　understanding　the　content　of　a　remote　sensing　image.　Specially,　a　high-resolution　remote　sensing　scene　contains　rich　information　and　complex　content.　Considering　that　the　scene　content　in　a　remote　sensing　image　is　very　tight　to　the　spatial　relationship　characteristics,　how　to　design　an　effective　feature　extraction　network　directly　decides　the　quality　of　classification　by　fully　mining　the　spatial　information　in　a　high-resolution　remote　sensing　image.　In　recent　years,　convolutional　neural　networks　(CNNs)　have　achieved　excellent　performance　in　remote　sensing　image　classification,　especially　the　residual　dense　network　(RDN)　as　one　of　the　representative　networks　of　CNN,　which　shows　a　stronger　feature　learning　ability　as　it　fully　utilizes　all　the　convolutional　layer　information.　Therefore,　we　design　an　RDN　based　on　channel-spatial　attention　for　scene　classification　of　a　high-resolution　remote　sensing　image.　First,　multi-layer　convolutional　features　are　fused　with　residual　dense　blocks.　Then,　a　channel-spatial　attention　module　is　added　to　obtain　more　effective　feature　representation.　Finally,　softmax　classifier　is　applied　to　classify　the　scene　after　adopting　data　augmentation　strategy　for　meeting　the　training　requirements　of　the　network　parameters.　Five　experiments　are　conducted　on　the　UC　Merced　Land-Use　Dataset　(UCM)　and　Aerial　Image　Dataset　(AID),　and　the　competitive　results　demonstrate　that　our　method　can　extract　more　effective　features　and　is　more　conducive　to　classifying　a　scene.