Spatial-Spectral Adaptive Graph Convolutional Subspace Clustering for Hyperspectral Image

Graph convolution subspace clustering has been widely used in the field of hyperspectral image (HSI) unsupervised classification due to its ability to aggregate neighborhood information. However, existing methods focus on using graph convolution techniques to design feature extraction functions, ignoring the mutual optimization of the graph convolution operator and the self-expression coefficient matrix, leading to suboptimal clustering results. In addition, these methods directly construct graphs on raw data, which may be easily affected by noises and then degrade the clustering performance, as the constructed topology is not credible for the training procedure. To address these issues, we propose a novel method called spatial-spectral adaptive graph convolutional subspace clustering (S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>AGCSC). We employ the reconstruction coefficient matrix to devise a graph convolutional operator with adjacency matrix, which collaboratively computes both the feature representations and coefficient matrix, and the graph-convolutional operator is updated iteratively and adaptively during training. In addition, we harness a combination of spectral and spatial features to introduce additional view information to help learn more robust features and generate more refined superpixels. Experimental validation on three HSI datasets confirms the efficacy of S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>AGCSC.