A multiscale graph convolutional network for change detection in homogeneous and heterogeneous remote sensing images

Abstract

To date, although numerous methods of Change detection (CD) in remote sensing images have been proposed, accurately identifying changes is still a great challenge, due to the difficulties in effectively modeling the features from ground objects with different patterns. In this paper, a novel CD method based on the graph convolutional network (GCN) and multiscale object-based technique is proposed for both homogeneous and heterogeneous images. First, the object-wise high level features are obtained through a pre-trained U-net and the multiscale segmentations. Second, by treating each parcel as a node, the graph representations can be formed and then fed into the proposed multiscale graph convolutional network with each channel corresponding to one scale. The multiscale GCN propagates the label information from a small amount of labeled nodes to the other unlabeled ones. Finally, to comprehensively incorporate the information from the output channels of multiscale GCN, a fusion strategy is designed using the parent–child relationships between scales. Extensive experiments on optical, SAR and heterogeneous optical/SAR data sets demonstrate that the proposed method outperforms some state-of-the-art methods in both qualitative and quantitative evaluations.

Keywords

• Change detection
• Graph convolutional network
• Multiscale segmentation
• Semisupervised
• Remote sensing images