Parallelizing maximum likelihood classification on computer cluster and graphics processing unit for supervised image classification

Parallelizing maximum likelihood classification on computer cluster and graphics processing unit for supervised image classification

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Supervised image classification has been widely utilized in a variety of remote sensing applications.When large volume of satellite imagery data and aerial photos are increasingly available, high-performance image processing solutions are required to handle large scale of data.This paper introduces how maximum likelihood Mushroom Drinks classification approach is parallelized for implementation on a computer Downstems cluster and a graphics processing unit to achieve high performance when processing big imagery data.

The solution is scalable and satisfies the need of change detection, object identification, and exploratory analysis on large-scale high-resolution imagery data in remote sensing applications.