Classifying　land-use　scenes　from　high-resolution　remote-sensing　imagery　with　high　quality　and　accuracy　is　of　paramount　interest　for　science　and　land　management　applications.　In　this　article,　we　proposed　a　new　model　for　land-use　scene　classification　by　integrating　the　recent　success　of　convolutional　neural　network　(CNN)　and　constrained　extreme　learning　machine　(CELM).　In　the　model,　the　fully　connected　layers　of　a　pretrained　CNN　have　been　removed.　Then,　CNN　works　as　a　deep　and　robust　convolutional　feature　extractor.　After　normalization,　deep　convolutional　features　are　fed　to　the　CELM　classifier.　To　analyse　the　performance,　the　proposed　method　has　been　evaluated　on　two　challenging　high-resolution　data　sets:　(1)　the　aerial　image　data　set　consisting　of　30　different　aerial　scene　categories　with　sub-metre　resolution　and　(2)　a　Sydney　data　set　that　is　a　large　high　spatial　resolution　satellite　image.　Experimental　results　show　that　the　CNN-CELM　model　improves　the　generalization　ability　and　reduces　the　training　time　compared　to　state-of-the-art　methods.