GPU-accelerated MRF segmentation algorithm for SAR images

Authors:
Haigang Sui;Feifei Peng;Chuan Xu;Kaimin Sun;Jianya Gong
Affiliations:
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei 430079, China;The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei 430079, China and Institute of Survey and Mapping of Tianjin, Tianjin 300381, ...;The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei 430079, China;The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei 430079, China;The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Hubei 430079, China
Venue:
Computers & Geosciences
Year:
2012

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Abstract

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 signi@?cant 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.