  Random Ferns -- as a less known example of Ensemble Learning -- have been
successfully applied in many Computer Vision applications ranging from keypoint
matching to object detection. This paper extends the Random Fern framework to
the semantic segmentation of polarimetric synthetic aperture radar images. By
using internal projections that are defined over the space of Hermitian
matrices, the proposed classifier can be directly applied to the polarimetric
covariance matrices without the need to explicitly compute predefined image
features. Furthermore, two distinct optimization strategies are proposed: The
first based on pre-selection and grouping of internal binary features before
the creation of the classifier; and the second based on iteratively improving
the properties of a given Random Fern. Both strategies are able to boost the
performance by filtering features that are either redundant or have a low
information content and by grouping correlated features to best fulfill the
independence assumptions made by the Random Fern classifier. Experiments show
that results can be achieved that are similar to a more complex Random Forest
model and competitive to a deep learning baseline.
