Saran, Ayse NurdanNar, FatihOzgur, Atilla2017-03-072025-09-182017-03-072025-09-182016Nar, F., Özgür,A., Saran, A.N. (2016). Sparsity-driven change detection in multitemporal sar images. IEEE Geoscience And Remote Sensing Letters, 13(7), 1032-1036. http://dx.doi.org/10.1109/LGRS.2016.25620321545-598X1558-0571https://doi.org/10.1109/LGRS.2016.2562032https://hdl.handle.net/20.500.12416/11685Ozgur, Atilla/0000-0002-9237-8347; Nar, Fatih/0000-0002-3003-8136In 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.eninfo:eu-repo/semantics/closedAccessChange DetectionImage AnalysisLog RatioSynthetic Aperture Radar (Sar)Total Variation (Tv)L(1)-NormArticle10.1109/LGRS.2016.25620322-s2.0-84971441880