  The analysis of Synthetic Aperture Radar (SAR) imagery is an important step
in remote sensing applications, and it is a challenging problem due to its
inherent speckle noise. One typical solution is to model the data using the
$G_I^0$ distribution and extract its roughness information, which in turn can
be used in posterior imaging tasks, such as segmentation, classification and
interpretation. This leads to the need of quick and reliable estimation of the
roughness parameter from SAR data, especially with high resolution images.
Unfortunately, traditional parameter estimation procedures are slow and prone
to estimation failures. In this work, we proposed a neural network-based
estimation framework that first learns how to predict underlying parameters of
$G_I^0$ samples and then can be used to estimate the roughness of unseen data.
We show that this approach leads to an estimator that is quicker, yields less
estimation error and is less prone to failures than the traditional estimation
procedures for this problem, even when we use a simple network. More
importantly, we show that this same methodology can be generalized to handle
image inputs and, even if trained on purely synthetic data for a few seconds,
is able to perform real time pixel-wise roughness estimation for high
resolution real SAR imagery.
