  In the context of Earth observation, change detection boils down to comparing
images acquired at different times by sensors of possibly different spatial
and/or spectral resolutions or different modalities (e.g., optical or radar).
Even when considering only optical images, this task has proven to be
challenging as soon as the sensors differ by their spatial and/or spectral
resolutions. This paper proposes a novel unsupervised change detection method
dedicated to images acquired by such so-called heterogeneous optical sensors.
It capitalizes on recent advances which formulate the change detection task
into a robust fusion framework. Adopting this formulation, the work reported in
this paper shows that any off-the-shelf network trained beforehand to fuse
optical images of different spatial and/or spectral resolutions can be easily
complemented with a network of the same architecture and embedded into an
adversarial framework to perform change detection. A comparison with
state-of-the-art change detection methods demonstrates the versatility and the
effectiveness of the proposed approach.
