  High-dimensional and complex spectral structures make clustering of
hy-perspectral images (HSI) a challenging task. Subspace clustering has been
shown to be an effective approach for addressing this problem. However, current
subspace clustering algorithms are mainly designed for a single view and do not
fully exploit spatial or texture feature information in HSI. This study
proposed a multiview subspace clustering of HSI based on graph convolutional
networks. (1) This paper uses the powerful classification ability of graph
convolutional network and the learning ability of topologi-cal relationships
between nodes to analyze and express the spatial relation-ship of HSI. (2)
Pixel texture and pixel neighbor spatial-spectral infor-mation were sent to
construct two graph convolutional subspaces. (3) An attention-based fusion
module was used to adaptively construct a more discriminative feature map. The
model was evaluated on three popular HSI datasets, including Indian Pines,
Pavia University, and Houston. It achieved overall accuracies of 92.38%,
93.43%, and 83.82%, respectively and significantly outperformed the
state-of-the-art clustering methods. In conclusion, the proposed model can
effectively improve the clustering ac-curacy of HSI.
