This　paper　addresses　the　challenges　of　high　model　complexity　and　low　classification　accuracy　in　remote　sensing　image　classification　using　convolutional　neural　networks.　To　overcome　these　challenges,　a　modified　DeeplabV3+　network　is　proposed,　which　replaces　the　deep　feature　extractor　in　the　encoder　with　lightweight　networks　MobilenetV2　and　Xception_65.　The　decoder　structure　is　also　modified　to　feature　fusion　layer　by　layer　in　order　to　refine　the　up-sampling　process　in　the　decoding　region.　In　addition,　a　channel　attention　module　is　introduced　to　strengthen　the　information　association　between　codecs,　and　multiscale　supervision　is　used　to　adapt　the　receptive　field.　Four　networks　with　different　encoding　and　decoding　structures　are　constructed　and　verified　on　the　CCF　dataset.　The　experimental　results　show　that　the　MS-XDeeplabV3+　network,　which　uses　Xception_65　in　the　encoder　and　layer　by　layer　connection,　channel　attention　module,　and　multiscale　supervision　in　the　decoder,　has　reduced　number　of　model　parameters,　faster　training　speed,　refined　edge　information　for　ground　objects,　and　improved　classification　accuracy　for　grassland　and　linear　ground　objects　such　as　roads　and　water　bodies.　The　overall　pixel　accuracy　and　Kappa　coefficient　of　the　MS-XDeeplabV3+　network　reach　0.　9122　and　0.　8646,　respectively,　which　show　the　best　performance　among　all　networks　in　remote　sensing　image　classification.　©　2023　Universitat　zu　Koln.　All　rights　reserved.