6485

A New Tool for Classification of Satellite Images Available from Google Maps: Efficient Implementation in Graphics Processing Units

Sergio Bernabe, Antonio Plaza
Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, Avda. de la Universidad s/n, E-10071 Caceres, Spain
XXII Jornadas de Paralelismo, 2011

@InProceedings{JP2011_0003,

   title={A New Tool for Classification of Satellite Images Available from Google Maps: Efficient Implementation in Graphics Processing Units},

   author={S. Bernabe and A. Plaza},

   booktitle={Actas XXII Jornadas de Paralelismo {(JP2011)}},

   pages={101–106},

   year={2011},

   editor={F. Almeida and V. Blanco and C. Leon and C. Rodriguez and F. de Sande},

   affiliation={Universidad de Extremadura},

   month={sep},

   publisher={Universidad de La Laguna},

   OPTnote={ISBN: 978-84-694-1791-1},

   isbn={978-84-694-1791-1},

   localfile={PaperJP2011_sbernabe.pdf}

}

Download Download (PDF)   View View   Source Source   

899

views

In this work, we develop a new parallel implementation of the k-means unsupervised clustering algorithm for commodity graphic processing units (GPUs), and further evaluate the performance of this newly developed algorithm in the task of classifying (in unsupervised fashion) satellite imagery available from Google Maps engine. With the ultimate goal of evaluating the classification precision of the newly developed algorithm, we have analyzed the consensus or agreement in the classification achieved by our implementation and an alternative implementation of the algorithm available in commercial software. Our experimental results, conducted using satellite images obtained from Google Maps engine over different locations around the Earth, indicate that the classification agreement between our parallel version and the k-means algorithm available in commercial software is very high. In addition, the GPU version (developed using the CUDA language available from NVidiaTM) is much faster that the serial one (speedup above 30), thus indicating that our proposed implementation allows for larger scale processing of high-dimensional image databases such as those available in the Google Maps engine.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: