  Although domain adaptation has been extensively studied in natural
image-based segmentation task, the research on cross-domain segmentation for
very high resolution (VHR) remote sensing images (RSIs) still remains
underexplored. The VHR RSIs-based cross-domain segmentation mainly faces two
critical challenges: 1) Large area land covers with many diverse object
categories bring severe local patch-level data distribution deviations, thus
yielding different adaptation difficulties for different local patches; 2)
Different VHR sensor types or dynamically changing modes cause the VHR images
to go through intensive data distribution differences even for the same
geographical location, resulting in different global feature-level domain gap.
To address these challenges, we propose a curriculum-style local-to-global
cross-domain adaptation framework for the segmentation of VHR RSIs. The
proposed curriculum-style adaptation performs the adaptation process in an
easy-to-hard way according to the adaptation difficulties that can be obtained
using an entropy-based score for each patch of the target domain, and thus well
aligns the local patches in a domain image. The proposed local-to-global
adaptation performs the feature alignment process from the locally semantic to
globally structural feature discrepancies, and consists of a semantic-level
domain classifier and an entropy-level domain classifier that can reduce the
above cross-domain feature discrepancies. Extensive experiments have been
conducted in various cross-domain scenarios, including geographic location
variations and imaging mode variations, and the experimental results
demonstrate that the proposed method can significantly boost the domain
adaptability of segmentation networks for VHR RSIs. Our code is available at:
https://github.com/BOBrown/CCDA_LGFA.
