In this study, we investigate the classification performance of two clustering algorithms, the fuzzy C-means (FCM) and hierarchical clustering analysis (HCA) algorithms applied to crop type classification of high-resolution airborne synthetic aperture radar (SAR) imagery based on Haralick and autocorrelation textural features. The contribution of the different polarization channels toward the overall classification of different cluster regions are also analyzed as well as the influence in the election of the optimum parameters for wavelet image enhancement.