  Image translation with convolutional autoencoders has recently been used as
an approach to multimodal change detection in bitemporal satellite images. A
main challenge is the alignment of the code spaces by reducing the contribution
of change pixels to the learning of the translation function. Many existing
approaches train the networks by exploiting supervised information of the
change areas, which, however, is not always available. We propose to extract
relational pixel information captured by domain-specific affinity matrices at
the input and use this to enforce alignment of the code spaces and reduce the
impact of change pixels on the learning objective. A change prior is derived in
an unsupervised fashion from pixel pair affinities that are comparable across
domains. To achieve code space alignment we enforce that pixel with similar
affinity relations in the input domains should be correlated also in code
space. We demonstrate the utility of this procedure in combination with cycle
consistency. The proposed approach are compared with state-of-the-art deep
learning algorithms. Experiments conducted on four real datasets show the
effectiveness of our methodology.

Computer Vision and Pattern Recognition