M. Forouzanfar; H. Abrishami-Moghaddam; M. Dehghani
Abstract
Despite the wide application of SAR images in lineaments extraction, DEM generation and displacements determination, their radiometric quality and interpretability is degraded due to the presence of a multiplicative noise called speckle. Therefore, the enhancement of SAR images is an important step before ...
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Despite the wide application of SAR images in lineaments extraction, DEM generation and displacements determination, their radiometric quality and interpretability is degraded due to the presence of a multiplicative noise called speckle. Therefore, the enhancement of SAR images is an important step before using them in any application. In this paper, a new image enhancement method tailored to SAR images is proposed. In this method, the logarithmically transformed SAR image is decomposed using the dual-tree complex wavelet transform (DTCWT).In order to effectively extract the wavelet interscale dependencies, the signal component of wavelet coefficients is modeled with an isotropic stable distribution, while the noise component is approximated using an isotropic Gaussian model. A bivariate Bayesian estimator is then designed to effectively remove speckle from noisy coefficients in the complex wavelet domain. Both quantitative and qualitative comparisons of the proposed method with new speckle reduction methods, demonstrate its higher performance in speckle reduction from SAR images
M. Dehghani; M. J. Valadan Zouj; A. Mansourian
Abstract
study several 2D and 3D math models have been tested in order to correct slant range SAR data geometrically. Some of these models consider the imaging geometry at the time of imaging while the others relate the ground space to the image one by mathematical polynomials. The images used here are 3 ENVISAT ...
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study several 2D and 3D math models have been tested in order to correct slant range SAR data geometrically. Some of these models consider the imaging geometry at the time of imaging while the others relate the ground space to the image one by mathematical polynomials. The images used here are 3 ENVISAT ones of Bam area. In order to extract the 3D GCPs, a topographic map with a scale of 1:25000 and SRTM DEM were used. The 2D math models used in this study include Global polynomial, Point wise, Piece wise and Projective while the 3D models are DLT and Rigorous SAR model. Since the images used in this study were originally ordered for interferometry studies, their baseline is so small that the precision of 3D coordinates extraction is not satisfactory enough. However, the results of 2D models are much better.
