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GPU-accelerated MRF segmentation algorithm for SAR images

Haigang Sui, Feifei Peng, Chuan Xu, Kaimin Sun, Jianya Gong
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei 430079, China
Computers & Geosciences, Volume 43, Pages 159-166, 2012

@article{sui2012gpu,

   title={GPU-accelerated MRF segmentation algorithm for SAR images},

   author={Sui, H. and Peng, F. and Xu, C. and Sun, K. and Gong, J.},

   journal={Computers & Geosciences},

   year={2012},

   publisher={Elsevier}

}

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Markov Random Field (MRF) approaches have been widely studied for Synthetic Aperture Radar (SAR) image segmentation, but they have a large computational cost and hence are not widely used in practice. Fortunately parallel algorithms have been documented to enjoy significant speedups when ported to run on a graphics processing units (GPUs) instead of a standard CPU. Presented here is an implementation of graphics processing units in General Purpose Computation (GPGPU) for SAR image segmentation based on the MRF method, using the C-oriented Compute Unified Device Architecture (CUDA) developed by NVIDIA. This experiment with GPGPU shows that the speed of segmentation can be increased by a factor of 10 for large images.
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