Superpixel-based graph convolutional neural network for polarimetric synthetic aperture radar image classification

The superpixel-based graph convolutional network with local and global information (SGCN-LG) is introduced for polarimetric synthetic aperture radar (PolSAR) image classification in this paper. The number of superpixels (SPs) is automatically determined according to analyzing the second order differences of the standard deviations of pixels within the square patches with various sizes. The local graph is constructed using neighboring SPs where the number of neighbors for each SP is automatically determined by considering multiple hyper windows around all composing pixels of that SP. Moreover, the nearest neighboring SPs from all classes are chosen from the entire scene to construct the global graph containing the discrimination information and relationship among labeled SPs. The local and global features are fused to achieve the classification map. According to experimental results, the proposed SGCN-LG model outperforms several powerful PolSAR classification models.