One of the major factors plaguing the performance of synthetic aperture radar (SAR) imagery is the presence of signal-dependent, speckle noise. Grainy in appearance, speckle noise is primarily due to the phase fluctuations of the electromagnetic returned signals. Since the inherent spatial-correlation characteristics of speckle in SAR images are not exploited in existing multiplicative models for speckle noise, a new approach is proposed here that provides a new mathematical framework for modeling and mitigation of speckle noise. The contribution of this paper is twofold. First, a novel model for speckled SAR imaging is introduced based on Markov random fields (MRFs) in conjunction with statistical optics. Second, utilizing the model, a global energy-minimization algorithm based on simulated annealing (SA), is introduced for speckle reduction. In particular, the joint conditional probability density function (cpdf) of the intensity of any two points in the speckled image and the associated correlation function are used to derive the cpdf of any center pixel intensity given its four neighbors. The Hammersley-Clifford theorem is then used to derive the energy function associated with the MRF. The SA, built on the Metropolis sampler, is employed for speckle reduction. Four metrics are used to assess the quality of the speckle reduction: the mean-square error, SNR, an edge-preservation parameter and the equivalent number of looks. A comparative study using both simulations and real SAR images indicates that the proposed approach performs better in comparison to filtering techniques such as the Gamm Map, the modified Lee and the enhanced Frost algorithms.
Lankoande, Ousseini; Majeed M. Hayat; and Bal Santhanam. "Speckle Reduction of SAR Images Based on a Combined Markov Random Field and Statistical Optics Approach (Version1)." (2004). https://digitalrepository.unm.edu/ece_rpts/14