The influence of distances in NLM Polsar filters

Abstract

The NLM (Nonlocal Means) approach is an effective framework for noise reduction. It relies on building, for each pixel, a convolution matrix whose entries are measures of similarities between patches. Such measures have to be computed between positive-definite Hermitian matrices when it comes to Polarimetric Synthetic Aperture Radar (PolSAR) imagery. Speckle reduction in this kind of images is a difficult task, as it is expected that several properties are well preserved by the filter. Torres et al. (2014) used a test statistic between Wishart distributions based on the Hellinger distance to compute such measures of similarity. In this work we assess the impact of using this and two other stochastic distances (Kullback-Leibler and Bhattacharya) under the same framework. The comparison is made using mean preservation, equivalent number of looks, edge correlation and the structural similarity index.