Spectral-Spatial Superpixel Anchor Graph-Based Clustering for Hyperspectral Imagery

Hyperspectral image (HSI) clustering has attracted great attention in the field of remote sensing. General anchor-based clustering methods often suffer from the problems of unstable anchor selection and insufficient utilization of spatial information, resulting in poor clustering performance. In this letter, a spectral-spatial superpixel anchor graph-based clustering (S3AGC) method is proposed for HSIs. Specifically, S3AGC further improves the clustering performance by simultaneously considering spatial and structural information as well as an advanced anchor selection strategy. Based on the spatial distribution, a useful HSI denoising solution is presented to reduce noise interference, and an effective anchor selection strategy is raised to alleviate the instability of random or clustering selection. Besides, we use a graph convolution method to embed structural information of spectral bands into the proposed framework. Experiments on HSI datasets verify the effectiveness of S3AGC.