Leveraging Convolutional and Graph Networks for an Unsupervised Remote Sensing Labelling Tool
To address the heavy reliance on expert intervention and pre-annotated data in remote sensing image labeling, this paper proposes an unsupervised automatic annotation method for Sentinel-2 imagery. The method first performs initial image segmentation by jointly modeling color and spatial similarity, constructing a region-level graph structure. It then introduces a rotation-invariant graph neural network to aggregate neighborhood topological relationships and learn robust unsupervised feature representations. Finally, it enables efficient clustering of homogeneous geographic regions and semantically consistent labeling. Unlike conventional supervised paradigms, the approach requires no prior labels, significantly improving annotation efficiency, scalability, and interpretability. Experimental results demonstrate superior performance in fine-grained land-cover classification and outlier suppression, validating its effectiveness for large-scale, label-free remote sensing analysis.