Pixel-wise classification plays an important role for image interpretation. The remote sensing images, especially polarimetric synthetic aperture radar (PolSAR), have provided wide applications for both military and civilian users regardless of weather or lighting conditions. However, the classification of heterogeneous imagery is still challenging. In this paper, we propose a novel deep fuzzy graph convolutional network (DFGCN) for pixel-wise classification of PolSAR imagery. Inspired by the

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... zzy logic, the imagery (i.e., a reflection of the backscattering of the ground) is represented by a fuzzy graph, whose node is associated with a feature vector, and fuzzy weights denoting the similarity degrees between nodes are defined by using both feature distance and spatial relation. We also construct a new graph residual module stacking multiple residual layers to extend the depth of our fuzzy graph network. Moreover, we present a graph shrinking strategy to reduce the graph size for training and predicting the labels of large-scale PolSAR imagery, which can cut down the computational cost. The experimental results on various real-world PolSAR datasets indicate that DFGCN dramatically enhances classification accuracies on both heterogeneous and homogeneous regions with limited labeled pixels compared with the state-of-the-art methods.