  Change detection (CD) in remote sensing images has been an ever-expanding
area of research. To date, although many methods have been proposed using
various techniques, accurately identifying changes is still a great challenge,
especially in the high resolution or heterogeneous situations, 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. 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 number of labeled nodes to the other ones which
are unlabeled. Further, to comprehensively incorporate the information from the
output channels of multiscale GCN, a fusion strategy is designed using the
father-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. Besides, the Influences of some factors are also
discussed.
