  Semantic segmentation has achieved significant advances in recent years.
While deep neural networks perform semantic segmentation well, their success
rely on pixel level supervision which is expensive and time-consuming. Further,
training using data from one domain may not generalize well to data from a new
domain due to a domain gap between data distributions in the different domains.
This domain gap is particularly evident in aerial images where visual
appearance depends on the type of environment imaged, season, weather, and time
of day when the environment is imaged. Subsequently, this distribution gap
leads to severe accuracy loss when using a pretrained segmentation model to
analyze new data with different characteristics. In this paper, we propose a
novel unsupervised domain adaptation framework to address domain shift in the
context of aerial semantic image segmentation. To this end, we solve the
problem of domain shift by learn the soft label distribution difference between
the source and target domains. Further, we also apply entropy minimization on
the target domain to produce high-confident prediction rather than using
high-confident prediction by pseudo-labeling. We demonstrate the effectiveness
of our domain adaptation framework using the challenge image segmentation
dataset of ISPRS, and show improvement over state-of-the-art methods in terms
of various metrics.
