  Remote sensing segmentation has a wide range of applications in environmental
protection, and urban change detection, etc. Despite the success of deep
learning-based remote sensing segmentation methods (e.g., CNN and Transformer),
they are not flexible enough to model irregular objects. In addition, existing
graph contrastive learning methods usually adopt the way of maximizing mutual
information to keep the node representations consistent between different graph
views, which may cause the model to learn task-independent redundant
information. To tackle the above problems, this paper treats images as graph
structures and introduces a simple contrastive vision GNN (SC-ViG) architecture
for remote sensing segmentation. Specifically, we construct a node-masked and
edge-masked graph view to obtain an optimal graph structure representation,
which can adaptively learn whether to mask nodes and edges. Furthermore, this
paper innovatively introduces information bottleneck theory into graph
contrastive learning to maximize task-related information while minimizing
task-independent redundant information. Finally, we replace the convolutional
module in UNet with the SC-ViG module to complete the segmentation and
classification tasks of remote sensing images. Extensive experiments on
publicly available real datasets demonstrate that our method outperforms
state-of-the-art remote sensing image segmentation methods.
