Parallelization of sparsity-driven change detection method
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2017
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IEEE
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Abstract
In this study, Sparsity-driven Change Detection (SDCD) method, which has been proposed for detecting changes in multitemporal synthetic aperture radar (SAR) images, is parallelized to reduce the execution time. Parallelization of the SDCD is realized using OpenMP on CPU and CUDA on GPU. Execution speed of the parallelized SDCD is shown on real-world SAR images. Our experimental results show that the computation time of the parallel implementation brings significant speed-ups.
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Change Detection, Synthetic Aperture Radar, Total Variation, Parallelization, OpenMP, GPU, CUDA
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Özgür, Atilla; Saran, Ayşe Nurdan; Nar, Fatih, "Parallelization of sparsity-driven change detection method", 2017 25th Signal Processing And Communications Applications Conference (SIU), (2017).
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2017 25th Signal Processing And Communications Applications Conference (SIU)
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