  Polarimetric Synthetic Aperture Radar (PolSAR) images are an important source
of information. Speckle noise gives SAR images a granular appearance that makes
interpretation and analysis hard tasks. A major issue is the assessment of
information content in these kind of images, and how it is affected by usual
processing techniques. Previous works have resulted in various approaches for
quantifying image information content. As Narayanan, Desetty, and
Reichenbach(2002) we study this problem from the classification accuracy
viewpoint, focusing in the filtering and the classification stages. Thus,
through classified images we verify how changing properties of the input data
affects their quality. Our input is an actual PolSAR image, the control
parameters are the filter (Local Mean or Model Based PolSAR, MBPolSAR), the
size of them and the classification method (Maximum Likelihood, ML, or Support
Vector Machine, SVM), and the output are the classification precision obtained
applying the classification algorithm to the filtered data. To expand the
conclusions, this study deals not only with Classification Accuracy, but also
with Kappa and Overall Accuracy as measures of map precision. Experiments were
conducted on two airborne PolSAR images. Unless Narayanan, Desetty, and
Reichenbach(2002) almost all measure values are good and increase with
degradation, i.e. the filtering algorithm that we used always improves the
classification results at least up to 7x7.
