MapReduce-based Parallel Learning for Large-scale Remote Sensing Images

ABSTRACT

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.

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