Browsing by Author "Ozgur, Atilla"

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    Parallelization of Sparsity-Driven Change Detection Method
    (Ieee, 2017) Ozgur, Atilla; Saran, Ayse Nurdan; Nar, Fatih
    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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    Citation - WoS: 13
    Citation - Scopus: 15
    Sparsity-Driven Change Detection in Multitemporal Sar Images
    (Ieee-inst Electrical Electronics Engineers inc, 2016) Saran, Ayse Nurdan; Nar, Fatih; Ozgur, Atilla
    In this letter, a method for detecting changes in multitemporal synthetic aperture radar (SAR) images by minimizing a novel cost function is proposed. This cost function is constructed with log-ratio-based data fidelity terms and an l(1)-norm-based total variation (TV) regularization term. Log-ratio terms model the changes between the two SAR images where the TV regularization term imposes smoothness on these changes in a sparse manner such that fine details are extracted while effects like speckle noise are reduced. The proposed method, sparsity-driven change detection (SDCD), employs accurate approximation techniques for the minimization of the cost function since data fidelity terms are not convex and the employed l(1)-norm TV regularization term is not differentiable. The performance of the SDCD is shown on real-world SAR images obtained from various SAR sensors.