One-Step Joint Learning of Self-Supervised Spectral Clustering With Anchor Graph and Fuzzy Clustering for Land Cover Classification

Spectral clustering, as an algorithm based on graph theory and spectral theory, has shown excellent performance in classification tasks of hyperspectral images in recent years. Although better results have been achieved, some challenges still exist. The inclusion of a priori information can increase the performance of spectral clustering algorithms; however, in practice, it is often unable to meet the demand for a priori information; at the same time, the two-stage structure of spectral clustering leads to bias and information loss during its clustering process. To address the above problems, we propose a one-step joint learning of self-supervised spectral clustering with anchor graph and fuzzy clustering. In this algorithm, we first use the BKHK method to generate anchor points that vary with the size of the dataset to enhance the quality of the similarity graph. After that, we use the idea of fuzzy clustering to optimize the membership and simultaneously learn the local and global structures of the data. Moreover, clustering information is integrated with pairwise constraint information in a unified update framework, which achieves mutual learning of clustering results and constraints under soft partitioning. After comparing with the existing algorithms, the proposed algorithm has better performance in classifying numerical datasets, remote sensing images, and hyperspectral datasets.