  Polarimetric Synthetic Aperture Radar (PolSAR) images are establishing as an
important source of information in remote sensing applications. The most
complete format this type of imaging produces consists of complex-valued
Hermitian matrices in every image coordinate and, as such, their visualization
is challenging. They also suffer from speckle noise which reduces the
signal-to-noise ratio. Smoothing techniques have been proposed in the
literature aiming at preserving different features and, analogously,
projections from the cone of Hermitian positive matrices to different color
representation spaces are used for enhancing certain characteristics. In this
work we propose the use of stochastic distances between models that describe
this type of data in a Nagao-Matsuyama-type of smoothing technique. The
resulting images are shown to present good visualization properties (noise
reduction with preservation of fine details) in all the considered
visualization spaces.
