Abstract: A variety of feature vectors are usually extracted from a polarimetric synthetic aperture radar (SAR) image and stacked directly into a highdimension feature vector to classify the different terrains in polarimetric SAR images, which results in the loss of some feature vectors’ discriminability. To address this problem, each feature vector is regarded as data from a different view of the image in this paper. Firstly, the polarimetric SAR image is oversegmented to obtain a number of superpixels, and five similarity matrices are respectively constructed from five feature vectors extracted from polarimetric SAR images based on superpixels. Secondly, consensus similarity network fusion, which belongs to the multiview learning algorithms, is used to generate a fused similarity matrix. Thirdly, spectral clustering is performed on the fused similarity matrix. Finally, a novel classification postprocessing strategy is proposed to correct the misclassified pixels. Extensive experimental results conducted on a simulated and a realworld polarimetric SAR images demonstrate the superiority of the proposed method, compared with five other classical methods.