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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Bibliographic Details
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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Summary: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.
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2015.7325718