Divided remote sensing image parallel processing method under Hadoop



The invention belongs to the technical field of image processing and discloses a divided remote sensing image parallel processing method under Hadoop. The method comprises the steps of deploying wireless sensors; selecting cluster heads; carrying out clustering; forming a simple map model by intra-cluster nodes, wherein the positions of all intra-cluster nodes in the clusters are obtained through S103, each node is taken as a vertex of a map, every two adjacent nodes are connected by a side, and the sample map model is a salient model of an image; calculating intra-cluster weights; carrying out network data verification after the intra-cluster weights are calculated; and carrying out cache management on a database after the network data verification is carried out. According to the method, the problem that a traditional SVM (Support Vector Machine) classification method is constructed based on a standalone environment, under the limitation of the computing capability of a CPU and a size of a memory, remote sensing image files only can be processed in series and very long time needs to be consumed is solved. The method is clear in image data processing effect and high in accuracy.




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