Parallelization of Sparsity-Driven Change Detection Method
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Date
2017
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Ieee
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Green Open Access
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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.
Description
Nar, Fatih/0000-0002-3003-8136
ORCID
Keywords
Change Detection, Synthetic Aperture Radar, Total Variation, Parallelization, Openmp, Gpu, Cuda
Fields of Science
0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
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Source
25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEY
Volume
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Start Page
1
End Page
4
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