  Existing deep learning-based change detection methods try to elaborately
design complicated neural networks with powerful feature representations, but
ignore the universal domain shift induced by time-varying land cover changes,
including luminance fluctuations and season changes between pre-event and
post-event images, thereby producing sub-optimal results. In this paper, we
propose an end-to-end Supervised Domain Adaptation framework for cross-domain
Change Detection, namely SDACD, to effectively alleviate the domain shift
between bi-temporal images for better change predictions. Specifically, our
SDACD presents collaborative adaptations from both image and feature
perspectives with supervised learning. Image adaptation exploits generative
adversarial learning with cycle-consistency constraints to perform cross-domain
style transformation, effectively narrowing the domain gap in a two-side
generation fashion. As to feature adaptation, we extract domain-invariant
features to align different feature distributions in the feature space, which
could further reduce the domain gap of cross-domain images. To further improve
the performance, we combine three types of bi-temporal images for the final
change prediction, including the initial input bi-temporal images and two
generated bi-temporal images from the pre-event and post-event domains.
Extensive experiments and analyses on two benchmarks demonstrate the
effectiveness and universality of our proposed framework. Notably, our
framework pushes several representative baseline models up to new
State-Of-The-Art records, achieving 97.34% and 92.36% on the CDD and WHU
building datasets, respectively. The source code and models are publicly
available at https://github.com/Perfect-You/SDACD.
