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首页> 外文期刊>Big Earth Data >Parallelizing maximum likelihood classification (MLC) for supervised image classification by pipelined thread approach through high-level synthesis (HLS) on FPGA cluster
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Parallelizing maximum likelihood classification (MLC) for supervised image classification by pipelined thread approach through high-level synthesis (HLS) on FPGA cluster

机译：通过FPGA集群上的高级综合（HLS）通过流水线线程方法并行化最大似然分类（MLC）进行监督图像分类

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摘要

High spectral, spatial, vertical and temporal resolution data are increasingly available and result in the serious challenge to process big remote-sensing images effectively and efficiently. This article introduced how to conduct supervised image classification by implementing maximum likelihood classification (MLC) over big image data on a field programmable gate array (FPGA) cloud. By comparing our prior work of implementing MLC on conventional cluster of multicore computers and graphics processing unit, it can be concluded that FPGAs can achieve the best performance in comparison to conventional CPU cluster and K40 GPU, and are more energy efficient. The proposed pipelined thread approach can be extended to other image-processing solutions to handle big data in the future.
机译：高光谱，空间，垂直和时间分辨率的数据越来越多，这对有效地处理大型遥感图像提出了严峻的挑战。本文介绍了如何通过在现场可编程门阵列（FPGA）云上对大图像数据实施最大似然分类（MLC）来进行监督图像分类。通过比较我们在多核计算机和图形处理单元的常规群集上实现MLC的先前工作，可以得出结论，与常规CPU群集和K40 GPU相比，FPGA可以实现最佳性能，并且更加节能。所提出的流水线线程方法可以扩展到其他图像处理解决方案，以在将来处理大数据。

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