Machine　learning　applied　to　large-scale　remote　sensing　images　shows　inadequacies　in　computational　capability　and　storage　space.　To　solve　this　problem,　we　propose　a　cloud　computing-based　scheme　for　learning　remote　sensing　images　in　a　parallel　manner:　(1)　a　hull　vector-based　hybrid　parallel　support　vector　machine　model　(HHB-PSVM)　is　proposed.　It　can　substantially　improve　the　efficiency　of　training　and　prediction　for　the　large-scale　samples　while　guaranteeing　classification　accuracy.　(2)　The　MapReduce　model　is　used　to　achieve　parallel　extraction　of　the　classification　features　for　the　remote　sensing　images,　and　the　MapReduce-based　HHB-PSVM　model　(MapReduce-HHB-PSVM)　is　used　to　implement　the　training　and　prediction　for　large-scale　samples.　(3)　MapReduce-HHB-PSVM　is　applied　to　land　use　classification,　enabling　various　types　of　land　use　to　be　classified　more　efficiently　by　using　fused　hyperspectral　images.　Experimental　results　show　that　MapReduce-HHB-PSVM　can　substantially　improve　classification　efficiency　of　large-scale　remote　sensing　images　while　guaranteeing　classification　accuracy,　and　it　can　promote　the　machine　interpretation　of　ground　objects　information　extracted　from　the　large-scale　remote　sensing　images　to　be　conducted　intelligently.　©　Fenghua　Huang.