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Graph Neural Networks (GNNs) have received increasing attention in manyfields. However, due to the lack of prior graphs, their use for semanticlabeling has been limited. Here, we propose a novel architecture called theSelf-Constructing Graph (SCG), which makes use of learnable latent variables togenerate embeddings and to self-construct the underlying graphs directly fromthe input features without relying on manually built prior knowledge graphs.SCG can automatically obtain optimized non-local context graphs fromcomplex-shaped objects in aerial imagery. We optimize SCG via an adaptivediagonal enhancement method and a variational lower bound that consists of acustomized graph reconstruction term and a Kullback-Leibler divergenceregularization term. We demonstrate the effectiveness and flexibility of theproposed SCG on the publicly available ISPRS Vaihingen dataset and our modelSCG-Net achieves competitive results in terms of F1-score with much fewerparameters and at a lower computational cost compared to related pure-CNN basedwork. Our code will be made public soon.

Self-Constructing Graph Convolutional Networks for Semantic Labeling