The mean shift image segmentation algorithm is very computationintensive. To address the need to deal with a large number of remotesensing (RS) image segmentations in real-world applications, this studyhas investigat...The mean shift image segmentation algorithm is very computationintensive. To address the need to deal with a large number of remotesensing (RS) image segmentations in real-world applications, this studyhas investigated the parallelization of the mean shift algorithm on asingle graphics processing unit (GPU) and a task-scheduling methodwith message passing interface (MPI)+OpenCL programming model on aGPU cluster platform. This paper presents the test results of the parallelmean shift image segmentation algorithm on Shelob, a GPU clusterplatform at Louisiana State University, with different datasets andparameters. The experimental results show that the proposed parallelalgorithm can achieve good speedups with different configurations andRS data and can provide an effective solution for RS image processingon a GPU cluster.展开更多
基金the Engineering Research Center of Geospatial Information and Digital Technology(NASG)(Wuhan University)[grant number SIDT20170601]Hubei Provincial Key Laboratory of Intelligent Geoinformation Processing(China University of Geosciences(Wuhan))[grant number KLIGIP2016A03]+2 种基金the Fundamental Research Funds for the Central Universities[grant number ZYGX2015J111]Key Laboratory of Spatial Data Mining&Information Sharing of the Ministry of Education(Fuzhou University)[grant number 2016LSDMIS06],[grant number 2017LSDMIS03]and also the National Science Foundation of the United States(Award Nos.1251095,1723292)。
文摘The mean shift image segmentation algorithm is very computationintensive. To address the need to deal with a large number of remotesensing (RS) image segmentations in real-world applications, this studyhas investigated the parallelization of the mean shift algorithm on asingle graphics processing unit (GPU) and a task-scheduling methodwith message passing interface (MPI)+OpenCL programming model on aGPU cluster platform. This paper presents the test results of the parallelmean shift image segmentation algorithm on Shelob, a GPU clusterplatform at Louisiana State University, with different datasets andparameters. The experimental results show that the proposed parallelalgorithm can achieve good speedups with different configurations andRS data and can provide an effective solution for RS image processingon a GPU cluster.
