High spectral,spatial,vertical and temporal resolution data are increasingly available and result in the serious challenge to pro-cess big remote-sensing images effectively and efficiently.This article introduced how ...High spectral,spatial,vertical and temporal resolution data are increasingly available and result in the serious challenge to pro-cess 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.展开更多
基金This research was partially supported by the National Science Foundation through the award SMA-1416509.
文摘High spectral,spatial,vertical and temporal resolution data are increasingly available and result in the serious challenge to pro-cess 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.
