Frequency Decomposition-Driven Unsupervised Domain Adaptation for Remote
  Sensing Image Semantic Segmentation
Xianping Ma Xiaokang Zhang Xingchen Ding Man-On Pun Siwei Ma
Cross-domain semantic segmentation of remote sensing (RS) imagery based on
unsupervised domain adaptation (UDA) techniques has significantly advanced
deep-learning applications in the geosciences. Recently, with its ingenious and
versatile architecture, the Transformer model has been successfully applied in
RS-UDA tasks. However, existing UDA methods mainly focus on domain alignment in
the high-level feature space. It is still challenging to retain cross-domain
local spatial details and global contextual semantics simultaneously, which is
crucial for the RS image semantic segmentation task. To address these problems,
we propose novel high/low-frequency decomposition (HLFD) techniques to guide
representation alignment in cross-domain semantic segmentation. Specifically,
HLFD attempts to decompose the feature maps into high- and low-frequency
components before performing the domain alignment in the corresponding
subspaces. Secondly, to further facilitate the alignment of decomposed
features, we propose a fully global-local generative adversarial network,
namely GLGAN, to learn domain-invariant detailed and semantic features across
domains by leveraging global-local transformer blocks (GLTBs). By integrating
HLFD techniques and the GLGAN, a novel UDA framework called FD-GLGAN is
developed to improve the cross-domain transferability and generalization
capability of semantic segmentation models. Extensive experiments on two
fine-resolution benchmark datasets, namely ISPRS Potsdam and ISPRS Vaihingen,
highlight the effectiveness and superiority of the proposed approach as
compared to the state-of-the-art UDA methods. The source code for this work
will be accessible at https://github.com/sstary/SSRS.