@article {44, title = {QMCTLS: Quasi Monte Carlo Texture Likelihood Sampling for Despeckling of Complex Polarimetric SAR Images}, journal = {IEEE Geoscience and Remote Sensing Letters}, volume = {12}, year = {2015}, chapter = {1566 - 1570}, abstract = {

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.

}, author = {F Li and L Xu and A Wong and D A. Clausi} }