Unsupervised classification of PolSAR image based on tensor product graph diffusion

摘要

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