Detection in most surveillance radars is based on the condition of point targets against a more or less homogeneous background. Currently, the resolution of many new types of radar is increasing, at least in the range dimension. Therefore many objects can no longer be considered as points. Also as a consequence, the background is becoming more diverse, in statistical terms. The scene addressed in this paper concerns a ground clutter environment, and extended objects observed with a polarimetric radar with modestly high resolution (i.e. 6 m in range). A step-by-step approach is proposed for the detection and parameter assessment of extended objects and adding classification based on polarimetric features. The evaluation of this approach is based on recordings of real natural scenes and artificially inserted extended objects. It has been observed that in multi-stage detection object classification benefits from several features, including polarimetric ones. It is proposed that the quality of the contour circumscribing the object is the prime factor for quality of features next to polarimetric features. Clutter is affecting, however, the edges of the contours, and therefore may have a major impact on features that are dependent on the shape of these contours. The results also suggest that in the case of large targets with a basically simple shape, like ships, the eccentricity of the shape of the extended object is consistent from scan to scan and probably could support the target tracking.