Unsupervised classification of polsar imagery based on consensus similarity network fusion

This paper proposes a PolSAR imagery unsupervised classification framework based on consensus similarity network fusion (CSNF), which is generally utilized for biomedical Sciences and for the first time used for PolSAR imagery classification in our work. First, the PolSAR image is divided into superpixels by a fast superpixel segmentation method and five groups of feature vectors are extracted based on the superpixels. Second, CSNF is performed on the five affinity matrixes constructed from the five groups of feature vectors to obtain a fused similarity matrix. Third, spectral clustering based on the fused similarity matrix is adopted to automatically achieve the classification results. Finally, a postprocessing procedure based on dissimilarity measure is performed to smooth the classification results and correct the misclassified pixels. The experimental results conducted on both a simulated PolSAR image and a real-world PolSAR image show the superiority of the proposed method.