title: Speckle suppression in SAR images using the 2-D GARCH model.
abstract: A novel Bayesian-based speckle suppression method for Synthetic Aperture Radar ( SAR) images is presented that preserves the structural features and textural information of the scene. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the wavelet coefficients of SAR images have significantly non-Gaussian statistics that are best described by the 2-D GARCH model. By using the 2-D GARCH model on the wavelet coefficients, we are capable of taking into account important characteristics of wavelet coefficients, such as heavy tailed marginal distribution and the dependencies between the coefficients. Furthermore, we use a maximum a posteriori (MAP) estimator for estimating the clean image wavelet coefficients. Finally, we compare our proposed method with various speckle suppression methods applied on synthetic and actual SAR images and we verify the performance improvement in utilizing the new strategy.
authors.affiliation: Amirkabir University of Technology, Department of Electrical Engineering, Tehran, Iran. mazlaghani@aut.ac.ir
authors.forename: Maryam
authors.initials: M
authors.lastname: Amirmazlaghani
authors.forename: Hamidreza
authors.initials: H
authors.lastname: Amindavar
authors.forename: Alireza
authors.initials: A
authors.lastname: Moghaddamjoo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011825:Radar; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity