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cvTile: Multilevel parallel geospatial data processing with OpenCV and CUDA

We are publishing an open source library to facilitate the use of three key image processing technologies (GDAL, OpenCV, CUDA) for scalable, high performance geospatial data processing. Herein, we present two computationally demanding algorithms for geospatial data processing which are commonly used...

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Main Authors:	Scott, Grant J., Angelov, Georgi A., Reinig, Michael L., Gaudiello, Eric C., England, Matthew R.
Format:	Conference Proceeding
Language:	English
Subjects:	Acceleration Algorithm design and analysis cvTile Data processing Geospatial analysis GPU Graphics processing units HPC Image Processing Kernel Libraries
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creator	Scott, Grant J. Angelov, Georgi A. Reinig, Michael L. Gaudiello, Eric C. England, Matthew R.
description	We are publishing an open source library to facilitate the use of three key image processing technologies (GDAL, OpenCV, CUDA) for scalable, high performance geospatial data processing. Herein, we present two computationally demanding algorithms for geospatial data processing which are commonly used for complex structural analysis of imagery. We show that processing time can be reduced by 98.1% and 84.3% for two computationally complex structural analysis algorithms using GPU co-processors over a pure CPU solution. It is our hope that the geoscience community will benefit from, and extend, this library; accelerating the development and integration of novel image processing, pattern recognition, and image information mining techniques.
doi_str_mv	10.1109/IGARSS.2015.7325718
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subjects	Acceleration Algorithm design and analysis cvTile Data processing Geospatial analysis GPU Graphics processing units HPC Image Processing Kernel Libraries
title	cvTile: Multilevel parallel geospatial data processing with OpenCV and CUDA
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