  The increasing availability of large-scale remote sensing labeled data has
prompted researchers to develop increasingly precise and accurate data-driven
models for land cover and crop classification (LC&CC). Moreover, with the
introduction of self-attention and introspection mechanisms, deep learning
approaches have shown promising results in processing long temporal sequences
in the multi-spectral domain with a contained computational request.
Nevertheless, most practical applications cannot rely on labeled data, and in
the field, surveys are a time consuming solution that poses strict limitations
to the number of collected samples. Moreover, atmospheric conditions and
specific geographical region characteristics constitute a relevant domain gap
that does not allow direct applicability of a trained model on the available
dataset to the area of interest. In this paper, we investigate adversarial
training of deep neural networks to bridge the domain discrepancy between
distinct geographical zones. In particular, we perform a thorough analysis of
domain adaptation applied to challenging multi-spectral, multi-temporal data,
accurately highlighting the advantages of adapting state-of-the-art
self-attention based models for LC&CC to different target zones where labeled
data are not available. Extensive experimentation demonstrated significant
performance and generalization gain in applying domain-adversarial training to
source and target regions with marked dissimilarities between the distribution
of extracted features.
