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Parallelizing maximum likelihood classification on computer cluster and graphics processing unit for supervised image classification
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DOI：10.1080/17538947.2016.1251502.png)
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Abstract
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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 classification approach is parallelized for implementation on a computer 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.
Keywords:
Maximum likelihood classification
supervised classification
parallel computing
graphics processing unit
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