Unsupervised classification of PolSAR image based on tensor product graph diffusion

This paper presents a new unsupervised classification framework based on tensor product graph (TPG) diffusion, which is generally utilized for optical image segmentation or image retrieval and for the first time used for PolSAR image classification in our work. First, the PolSAR image is divided into many superpixels by using a fast superpixel segmentation method. Second, seven features are extracted from the PolSAR image to form a feature vector based on segmented superpixels and construct a similarity matrix by using the Gaussian kernel. Third, TPG diffusion is performed on this similarity matrix to obtain a more discriminative similarity matrix by mining the higher order information between data points. Finally, spectral clustering based on diffused similarity matrix is adopted to automatically achieve the classification results. The experimental results conducted on both a simulated PolSAR image and a real-world PolSAR image demonstrate that our algorithm can effectively combine higher order neighborhood information and achieve higher classification accuracy.