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Unsupervised methods play an essential role in polarimetric synthetic aperture radar (PolSAR) image classification, where labeled data are difficult to obtain. However, there is still a large gap between existing unsupervised learning methods and supervised learning methods. Without the semantic constraints of labeled data, pixels within the same category are often misclassified into different categories, leaving the output to be messy. To address the previous issue, this article proposes a fully unsupervised pipeline for training convolutional neural networks (CNNs). The pipeline combines low-level superpixels and high-level CNN semantic features for high-quality pseudolabel generation. It effectively eliminates the misclassified pixels by voting within the superpixel blob while preserving the sharpness of edges. With the training process of the model, the quality of the generated labels is getting improved. Experiments on airborne [experimental airborne SAR system from Germany (ESAR)/airborne synthetic aperture radar from America (AIRSAR)] and spaceborne (RadarSat2) PolSAR images prove the effectiveness of the proposed method (measured with overall accuracy (OA), average accuracy (AA), and Kappa metrics). Our method outperforms the previous unsupervised methods (H/alpha-Wishart, SM-Wishart, FDD-H, DEC, and VQC-CAE) with a large margin and even has comparable performance to the supervised CNN model [fully CNN (FCN)]. [ABSTRACT FROM AUTHOR]