  Image translation with convolutional neural networks has recently been used
as an approach to multimodal change detection. Existing approaches train the
networks by exploiting supervised information of the change areas, which,
however, is not always available. A main challenge in the unsupervised problem
setting is to avoid that change pixels affect the learning of the translation
function. We propose two new network architectures trained with loss functions
weighted by priors that reduce the impact of change pixels on the learning
objective. The change prior is derived in an unsupervised fashion from
relational pixel information captured by domain-specific affinity matrices.
Specifically, we use the vertex degrees associated with an absolute affinity
difference matrix and demonstrate their utility in combination with cycle
consistency and adversarial training. The proposed neural networks are compared
with state-of-the-art algorithms. Experiments conducted on three real datasets
show the effectiveness of our methodology.
