Scene　classification　is　a　key　step　in　intelligent　information　processing　of　high-resolution　remote　sensing,　which　aims　to　identify　the　land-use　types　of　remote　sensing　image　blocks.　In　recent　years,　the　models　based　on　deep　convolution　neural　networks　(CNN)　have　made　outstanding　achievements　in　the　field　of　remote　sensing,　but　these　models　are　computationally　expensive　and　time-consuming,　while　the　lightweight　shallow　network　models　have　few　parameters,　fast　speed,　but　low　accuracy.　Both　the　deep　CNN　models　and　the　lightweight　shallow　network　models　could　not　be　directly　applied　to　embedded　devices.　Therefore,　we　propose　an　Adaptive　Enhanced　Knowledge　Distillation　(AE-KD)　to　deeply　mine　the　output　and　feature　information　from　teacher　model　and　transfer　them　to　student　model,　so　as　to　improve　the　performance　of　lightweight　model.　Firstly,　aiming　at　the　uneven　degree　of　difference　among　remote　sensing　image　categories,　an　adaptive　temperature　mechanism　is　proposed　by　improving　the　temperature　mechanism　in　the　traditional　knowledge　distillation,　which　promotes　the　student　model　to　better　learn　the　probability　distribution　knowledge　from　the　output　layer　of　the　large　and　deep　teacher　model.　And　then,　the　spatial　attention　and　inter-channel　correlation　of　features　are　added　as　constraints　in　order　to　make　the　student　model　learn　the　multi-level　knowledge　from　teacher　model.　The　experimental　results　on　UC　Merced　Land-Use　and　AID　public　datasets　show　that　the　proposed　method　reduces　91　%　parameters　of　the　teacher　model　and　improves　the　prediction　speed　by　22　times,　where　it　has　only　a　small　loss　of　classification　accuracy,　which　is　effective　for　the　lightweight　model.　ablation　study　also　further　analyzes　and　discusses　the　performance　improvement　of　the　student　model　under　different　levels　of　knowledge　distillation.　©　2022　IEEE.