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Versatile, robust and computational efficient methods for radar image segmentation, which preserve the full polarimetric information content, are of importance as research tools, as well as for practical applications in land surface monitoring. The method introduced here consists of several steps. The first step is a (reversible) transform of the full polarimetric radar information content into nine backscatter intensity values. The next steps relate to unsupervised clustering encompassing a simple region-growing segmentation (incomplete and over-segmented) followed by model-based agglomerative clustering and expectation-maximization on the pixels of these segments. Classification is achieved by Markov random field filtering on the original data. The result is a series of segmented maps, which differ in the number of (unsupervised) classes. For a (compatible) supervised approach, only the first and last step have to be applied. Results are discussed for the agricultural areas Flevoland in The Netherlands (AirSAR data) and DEMMIN in Germany, using the NASA/JPL AirSAR system and the DLR ESAR system, respectively. The applications include the use of groundtruth for legend development, the check for groundtruth completeness, and the construction of a bottom-up hierarchy of the characteristics that can be distinguished in the radar data. The latter gives important insights in physics of polarimetric radar backscattering mechanisms. Moreover, the relative importance of crop differences, (full-polarimetric) incidence angle effects and sub-classes (related to factors such as crop varieties, row direction or development stage) may be assessed. The overall classification results range between 84.3% and 98.0%, depending on number of observations dates and radar band(s) used, with higher values for the supervised approach, and substantially more thematic detail for the unsupervised approach.