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Change detection (CD) in remote sensing images has been an ever-expandingarea of research. To date, although many methods have been proposed usingvarious techniques, accurately identifying changes is still a great challenge,especially in the high resolution or heterogeneous situations, due to thedifficulties in effectively modeling the features from ground objects withdifferent patterns. In this paper, a novel CD method based on the graphconvolutional network (GCN) and multiscale object-based technique is proposedfor both homogeneous and heterogeneous images. First, the object-wise highlevel features are obtained through a pre-trained U-net and the multiscalesegmentations. Treating each parcel as a node, the graph representations can beformed and then, fed into the proposed multiscale graph convolutional networkwith each channel corresponding to one scale. The multiscale GCN propagates thelabel information from a small number of labeled nodes to the other ones whichare unlabeled. Further, to comprehensively incorporate the information from theoutput channels of multiscale GCN, a fusion strategy is designed using thefather-child relationships between scales. Extensive Experiments on optical,SAR and heterogeneous optical/SAR data sets demonstrate that the proposedmethod outperforms some state-of the-art methods in both qualitative andquantitative evaluations. Besides, the Influences of some factors are alsodiscussed.

A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images