  Deep convolutional neural networks (DCNNs) based remote sensing (RS) image
semantic segmentation technology has achieved great success used in many
real-world applications such as geographic element analysis. However, strong
dependency on annotated data of specific scene makes it hard for DCNNs to fit
different RS scenes. To solve this problem, recent works gradually focus on
cross-domain RS image semantic segmentation task. In this task, different
ground sampling distance, remote sensing sensor variation and different
geographical landscapes are three main factors causing dramatic domain shift
between source and target images. To decrease the negative influence of domain
shift, we propose a self-training guided disentangled adaptation network
(ST-DASegNet). We first propose source student backbone and target student
backbone to respectively extract the source-style and target-style feature for
both source and target images. Towards the intermediate output feature maps of
each backbone, we adopt adversarial learning for alignment. Then, we propose a
domain disentangled module to extract the universal feature and purify the
distinct feature of source-style and target-style features. Finally, these two
features are fused and served as input of source student decoder and target
student decoder to generate final predictions. Based on our proposed domain
disentangled module, we further propose exponential moving average (EMA) based
cross-domain separated self-training mechanism to ease the instability and
disadvantageous effect during adversarial optimization. Extensive experiments
and analysis on benchmark RS datasets show that ST-DASegNet outperforms
previous methods on cross-domain RS image semantic segmentation task and
achieves state-of-the-art (SOTA) results. Our code is available at
https://github.com/cv516Buaa/ST-DASegNet.
