  In the existing unsupervised domain adaptation (UDA) methods for remote
sensing images (RSIs) semantic segmentation, class symmetry is an widely
followed ideal assumption, where the source and target RSIs have exactly the
same class space. In practice, however, it is often very difficult to find a
source RSI with exactly the same classes as the target RSI. More commonly,
there are multiple source RSIs available. To this end, a novel class asymmetry
RSIs domain adaptation method with multiple sources is proposed in this paper,
which consists of four key components. Firstly, a multi-branch segmentation
network is built to learn an expert for each source RSI. Secondly, a novel
collaborative learning method with the cross-domain mixing strategy is
proposed, to supplement the class information for each source while achieving
the domain adaptation of each source-target pair. Thirdly, a pseudo-label
generation strategy is proposed to effectively combine strengths of different
experts, which can be flexibly applied to two cases where the source class
union is equal to or includes the target class set. Fourthly, a
multiview-enhanced knowledge integration module is developed for the high-level
knowledge routing and transfer from multiple domains to target predictions.
