  The domain adaptation (DA) approaches available to date are usually not well
suited for practical DA scenarios of remote sensing image classification, since
these methods (such as unsupervised DA) rely on rich prior knowledge about the
relationship between label sets of source and target domains, and source data
are often not accessible due to privacy or confidentiality issues. To this end,
we propose a practical universal domain adaptation setting for remote sensing
image scene classification that requires no prior knowledge on the label sets.
Furthermore, a novel universal domain adaptation method without source data is
proposed for cases when the source data is unavailable. The architecture of the
model is divided into two parts: the source data generation stage and the model
adaptation stage. The first stage estimates the conditional distribution of
source data from the pre-trained model using the knowledge of
class-separability in the source domain and then synthesizes the source data.
With this synthetic source data in hand, it becomes a universal DA task to
classify a target sample correctly if it belongs to any category in the source
label set, or mark it as ``unknown" otherwise. In the second stage, a novel
transferable weight that distinguishes the shared and private label sets in
each domain promotes the adaptation in the automatically discovered shared
label set and recognizes the ``unknown'' samples successfully. Empirical
results show that the proposed model is effective and practical for remote
sensing image scene classification, regardless of whether the source data is
available or not. The code is available at https://github.com/zhu-xlab/UniDA.
