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In recent years, Graph Neural Networks (GNN) have begun to receive extensive attention from researchers. Subsequently, ViG was proposed and its performance in learning irregular feature information in non-Euclidean data space was astonishing. Inspired by the success of ViG, we propose a GNN-based multi-scale fusion network model (GCNCD) to extract graph-level features for remote sensing building change detection (CD). GCNCD builds bitemporal images into a graph structure. It then learns richer features by aggregating the features (edge information) of neighbour vertices in the graph. To alleviate the over-smoothing problem caused by multi-layer graph convolution, the FNN module is used to improve the network’s ability to transform features and reduce the loss of spatial structure information. Compared with the traditional single-type feature fusion module, in the decoder, we perform feature fusion on adjacent-scale features and all scale features, respectively. It helps to promote information mobility and reduce spatial information loss. Our extensive experiments demonstrate the positive effects of graph convolution and fusion module in the field of remote sensing building change detection.

GCN-based multi-scale dual fusion for remote sensing building change detection