TY - GEN
T1 - MRF satellite image classification on GPU
AU - Valero-Lara, Pedro
PY - 2012
Y1 - 2012
N2 - One of the stages of the analysis of satellite images is given by a classification based on the Markov Random Fields (MRF) method. It is possible to find in literature several packages to carry out this analysis, and of course the classification tasks. One of them is the Orfeo Tool Box (OTB). The analysis of satellite images is an expensive computational task requiring real time execution or automatization. In order to reduce the execution time spent on the analysis of satellite images, parallelism techniques can be used. Currently, Graphics Processing Units (GPUs) are becoming a good choice to reduce the execution time of several applications at a low cost. In this paper, the author presents a GPU-based classification using MRF from the sequential algorithm that appears in the OTB package. The experimental results show a spectacular reduction of the execution time for the GPU-based algorithm, up to 225 times faster than the sequential algorithm included in the OTB package. Moreover, this result is also observed in the total power consumption, which is reduced by a significant amount.
AB - One of the stages of the analysis of satellite images is given by a classification based on the Markov Random Fields (MRF) method. It is possible to find in literature several packages to carry out this analysis, and of course the classification tasks. One of them is the Orfeo Tool Box (OTB). The analysis of satellite images is an expensive computational task requiring real time execution or automatization. In order to reduce the execution time spent on the analysis of satellite images, parallelism techniques can be used. Currently, Graphics Processing Units (GPUs) are becoming a good choice to reduce the execution time of several applications at a low cost. In this paper, the author presents a GPU-based classification using MRF from the sequential algorithm that appears in the OTB package. The experimental results show a spectacular reduction of the execution time for the GPU-based algorithm, up to 225 times faster than the sequential algorithm included in the OTB package. Moreover, this result is also observed in the total power consumption, which is reduced by a significant amount.
KW - Graphics Processing Units
KW - Markov Random Fields
KW - Orfeo Tool Box
KW - Satellite Imaging
UR - http://www.scopus.com/inward/record.url?scp=84871150569&partnerID=8YFLogxK
U2 - 10.1109/ICPPW.2012.24
DO - 10.1109/ICPPW.2012.24
M3 - Conference contribution
AN - SCOPUS:84871150569
SN - 9780769547954
T3 - Proceedings of the International Conference on Parallel Processing Workshops
SP - 149
EP - 156
BT - Proceedings - 41st International Conference on Parallel Processing Workshops, ICPPW 2012
T2 - 41st International Conference on Parallel Processing Workshops, ICPPW 2012
Y2 - 10 September 2012 through 13 September 2012
ER -