QMCTLS: Quasi Monte Carlo Texture Likelihood Sampling for Despeckling of Complex Polarimetric SAR Images

Despeckling of complex polarimetric SAR images is
more difficult than denoising of general images due to the low
signal-to-noise ratio and the complex signals. A novel stochastic
polarimetric SAR despeckling technique based on quasi Monte
Carlo sampling (QMCS) and region-based probabilistic similarity
likelihood has been developed. The despeckling of complex
polarimetric SAR images is formulated as a Bayesian least
squares optimization problem, where the posterior distribution
is estimated by QMCS in a nonparametric manner. The QMCS
approach allows the incorporation of the statistical description of
local texture pattern similarity. Experiments on two benchmark
quad-pol SAR images demonstrate that the proposed QMCTLS
filter outperforms referenced methods in terms of both noise
removal and detail preservation.