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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) Thedoi:10.2174/1874444301406011962 fatcat:rfbclm4qjbbdpbsu2euyshpgky