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Implementation of the parallel mean shift-based image segmentation algorithm on a GPU cluster
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DOI：10.1080/17538947.2018.1432709.png)
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The mean shift image segmentation algorithm is very computation-intensive. To address the need to deal with a large number of remote sensing (RS) image segmentations in real-world applications, this study has investigated the parallelization of the mean shift algorithm on a single graphics processing unit (GPU) and a task-scheduling method with message passing interface (MPI)+OpenCL programming model on a GPU cluster platform. This paper presents the test results of the parallel mean shift image segmentation algorithm on Shelob, a GPU cluster platform at Louisiana State University, with different datasets and parameters. The experimental results show that the proposed parallel algorithm can achieve good speedups with different configurations and RS data and can provide an effective solution for RS image processing on a GPU cluster.
Keyword:
Mean shift algorithm
GPU cluster
task scheduling
MPI
OpenCL
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Multisensor image fusion in remote sensing: concepts, methods and applications遥感多传感器图像融合: 概念、方法与应用
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