  Unsupervised multimodal change detection is a practical and challenging topic
that can play an important role in time-sensitive emergency applications. To
address the challenge that multimodal remote sensing images cannot be directly
compared due to their modal heterogeneity, we take advantage of two types of
modality-independent structural relationships in multimodal images. In
particular, we present a structural relationship graph representation learning
framework for measuring the similarity of the two structural relationships.
Firstly, structural graphs are generated from preprocessed multimodal image
pairs by means of an object-based image analysis approach. Then, a structural
relationship graph convolutional autoencoder (SR-GCAE) is proposed to learn
robust and representative features from graphs. Two loss functions aiming at
reconstructing vertex information and edge information are presented to make
the learned representations applicable for structural relationship similarity
measurement. Subsequently, the similarity levels of two structural
relationships are calculated from learned graph representations and two
difference images are generated based on the similarity levels. After obtaining
the difference images, an adaptive fusion strategy is presented to fuse the two
difference images. Finally, a morphological filtering-based postprocessing
approach is employed to refine the detection results. Experimental results on
five datasets with different modal combinations demonstrate the effectiveness
of the proposed method.
