One　of　the　challenging　issues　in　high-resolution　remote　sensing　images　is　classifying　land-use　scenes　with　high　quality　and　accuracy.　An　effective　feature　extractor　and　classifier　can　boost　classification　accuracy　in　scene　classification.　This　letter　proposes　a　deep-learning-based　classification　method,　which　combines　convolutional　neural　networks　(CNNs)　and　extreme　learning　machine　(ELM)　to　improve　classification　performance.　A　pretrained　CNN　is　initially　used　to　learn　deep　and　robust　features.　However,　the　generalization　ability　is　finite　and　suboptimal,　because　the　traditional　CNN　adopts　fully　connected　layers　as　classifier.　We　use　an　ELM　classifier　with　the　CNN-learned　features　instead　of　the　fully　connected　layers　of　CNN　to　obtain　excellent　results.　The　effectiveness　of　the　proposed　method　is　tested　on　the　UC-Merced　data　set　that　has　2100　remotely　sensed　land-use-scene　images　with　21　categories.　Experimental　results　show　that　the　proposed　CNN-ELM　classification　method　achieves　satisfactory　results.