Unsupervised Semantic Segmentation of PolSAR Images Based on Multiview Similarity

Semantic segmentation is an essential task in polarimetric synthetic aperture radar (PolSAR) image interpretation. To address the issue of insufficient measurement ability of single-view similarity, an unsupervised semantic segmentation method for PolSAR images based on multiview similarity is proposed to estimate the number-of-classes (NoC) and perform classification. NoC estimation is commonly neglected in semantic segmentation methods, to ensure rationality, a NoC estimation method before clustering is proposed based on multiview polarimetric rotation domain features and the visual assessment of tendency method for PolSAR images without supervision. Then, the norm distance, geodesic distance, maximum likelihood distance, and generalized likelihood ratio test distance based on the statistical characteristics of PolSAR images are comprehensively analyzed. Various advantages of different distances are integrated to combine the multiview vector information and scattering information to construct six multikernel similarity matrices. Subsequently, the consensus similarity network fusion method is utilized to further strengthen the discriminative ability of the similarity matrices. In addition, efficient superpixel segmentation is also adopted to reduce the speckle noise. Finally, based on the estimated NoC and the fused similarity matrix, spectral clustering is utilized to obtain semantic segmentation results. Extensive experiments are conducted on two AIRSAR datasets and one Gaofen-3 dataset demonstrate that the proposed method can effectively combine the spatial neighborhood similarity information and achieve higher semantic segmentation accuracy.