Superpixel Based Graph Convolutional Neural Network for SAR Image Segmentation

In this study, one of the state-of-the-art computer vision methods, namely superpixel-based graph convolutional networks (GCN), was used to achieve an accurate semantic segmentation of SAR images. In more detail, first, simple linear iterative clustering (SLIC) is used to over-segment the input SAR image into a set of superpixels, then a feature extraction method is employed to extract features from each of the superpixels. U-Net is used as deep feature extractors. Last, GCN architecture is used for node-based classification. We thus intend to exploit the spatial informationvia superpixels, as well the spatial relations among them via node edges. The experiments were conducted on real-world single polarization SAR images obtained from the Sentinel-1 satellite to test the performance of the proposed segmentation method. The results of these experiments show the advantage of the proposed GCN-based method for SAR image segmentation.