A new mathematical framework for modeling speckled imagery is introduced based on embedding the spatial correlation properties of speckled imagery, obtained from statistical optics, into a Markov-random-field (MRP) framework. The model is then used to perform speckle-noise reduction through the utilization of a global energy-minimization algorithm, which consists of simulated annealing in conjunction with the metropolis sampler algorithm. Comparative experimental results using both simulated as well as real synthetic-aperture-radar (SAR) imagery show that the proposed speckle-reduction technique outperforms existing speckle-noise filtering methods. This success is attributable to the ability of the proposed model to capture the physical spatial statistics of speckle within the confines of a MRF framework.
Speckle modeling and reduction in synthetic aperture radar imagery
IEEE International Conference on Image Processing 2005 ; 3 ; III-317
2005-01-01
2334489 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Speckle Modeling and Reduction in Synthetic Aperture Radar Imagery
British Library Conference Proceedings | 2005
|Speckle reduction in synthetic aperture radar imagery using wavelets [2034-41]
British Library Conference Proceedings | 1993
|Anisotropic diffusion for speckle filtering of polarimetric synthetic aperture radar imagery
British Library Online Contents | 2013
|Analysis of synthetic aperture radar imagery
NTRS | 1977
|Persistence modeling of angularly dependent synthetic aperture radar imagery
British Library Online Contents | 2008
|