Context - i.e. information not contained in a particular measurement but in its spatial proximity - plays a vital role in the analysis of images in general and in the semantic segmentation of Polarimetric Synthetic Aperture Radar (PolSAR) images in particular. Nevertheless, a detailed study on whether context should be incorporated implicitly (e.g. by spatial features) or explicitly (by exploiting classifiers tailored towards image analysis) and to which degree contextual information has a positive influence on the final classification result is missing in the literature. In this paper we close this gap by using projection-based Random Forests that allow to use various degrees of local context without changing the overall properties of the classifier (i.e. its capacity). Results on two PolSAR data sets - one airborne over a rural area, one space-borne over a dense urban area - show that local context indeed has substantial influence on the achieved accuracy by reducing label noise and resolving ambiguities. However, increasing access to local context beyond a certain amount has a negative effect on the obtained semantic maps.
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