  Recently, Graph Convolutional Network (GCN) has been widely used in
Hyperspectral Image (HSI) classification due to its satisfactory performance.
However, the number of labeled pixels is very limited in HSI, and thus the
available supervision information is usually insufficient, which will
inevitably degrade the representation ability of most existing GCN-based
methods. To enhance the feature representation ability, in this paper, a GCN
model with contrastive learning is proposed to explore the supervision signals
contained in both spectral information and spatial relations, which is termed
Contrastive Graph Convolutional Network (ConGCN), for HSI classification.
First, in order to mine sufficient supervision signals from spectral
information, a semi-supervised contrastive loss function is utilized to
maximize the agreement between different views of the same node or the nodes
from the same land cover category. Second, to extract the precious yet implicit
spatial relations in HSI, a graph generative loss function is leveraged to
explore supplementary supervision signals contained in the graph topology. In
addition, an adaptive graph augmentation technique is designed to flexibly
incorporate the spectral-spatial priors of HSI, which helps facilitate the
subsequent contrastive representation learning. The extensive experimental
results on four typical benchmark datasets firmly demonstrate the effectiveness
of the proposed ConGCN in both qualitative and quantitative aspects.
