  Graph Neural Networks (GNNs) have received increasing attention in many
fields. However, due to the lack of prior graphs, their use for semantic
labeling has been limited. Here, we propose a novel architecture called the
Self-Constructing Graph (SCG), which makes use of learnable latent variables to
generate embeddings and to self-construct the underlying graphs directly from
the input features without relying on manually built prior knowledge graphs.
SCG can automatically obtain optimized non-local context graphs from
complex-shaped objects in aerial imagery. We optimize SCG via an adaptive
diagonal enhancement method and a variational lower bound that consists of a
customized graph reconstruction term and a Kullback-Leibler divergence
regularization term. We demonstrate the effectiveness and flexibility of the
proposed SCG on the publicly available ISPRS Vaihingen dataset and our model
SCG-Net achieves competitive results in terms of F1-score with much fewer
parameters and at a lower computational cost compared to related pure-CNN based
work. Our code will be made public soon.
