Parallelizing maximum likelihood classification (MLC) for supervised image classification by pipelined thread approach through high-level synthesis (HLS) on FPGA cluster

Abstract

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.

Keywords

• FPGA
• maximum likelihood classification
• parallel computing