Online adaptation to distribution shifts in satellite image segmentation
stands as a crucial yet underexplored problem. In this paper, we address
source-free and online domain adaptation, i.e., test-time adaptation (TTA), for
satellite images, with the focus on mitigating distribution shifts caused by
various forms of image degradation. Towards achieving this goal, we propose a
novel TTA approach involving two effective strategies. First, we progressively
estimate the global Batch Normalization (BN) statistics of the target
distribution with incoming data stream. Leveraging these statistics during
inference has the ability to effectively reduce domain gap. Furthermore, we
enhance prediction quality by refining the predicted masks using global class
centers. Both strategies employ dynamic momentum for fast and stable
convergence. Notably, our method is backpropagation-free and hence fast and
lightweight, making it highly suitable for on-the-fly adaptation to new domain.
Through comprehensive experiments across various domain adaptation scenarios,
we demonstrate the robust performance of our method.

在线自适应卫星图像分割中的分布偏移问题是一个关键但尚未充分探索的问题。本文提出了一种新的在线测试时领域适应方法，通过两种有效策略来缓解由各种形式的图像退化引起的分布偏移。通过全局 BN 统计估计和全局类别中心修正预测掩码的方法，我们的方法在各种领域适应场景中展现了鲁棒的性能。

基于源自由的在线领域自适应卫星图像语义分割在图像退化下的应用

Source-Free Online Domain Adaptive Semantic Segmentation of Satellite  Images under Image Degradation

In this paper, we propose to tackle the problem of reducing discrepancies
between multiple domains referred to as multi-source domain adaptation and
consider it under the target shift assumption: in all domains we aim to solve a
classification problem with the same output classes, but with labels'
proportions differing across them. This problem, generally ignored in the vast
majority papers on domain adaptation papers, is nevertheless critical in
real-world applications, and we theoretically show its impact on the adaptation
success. To address this issue, we design a method based on optimal transport,
a theory that has been successfully used to tackle adaptation problems in
machine learning. Our method performs multi-source adaptation and target shift
correction simultaneously by learning the class probabilities of the unlabeled
target sample and the coupling allowing to align two (or more) probability
distributions. Experiments on both synthetic and real-world data related to
satellite image segmentation task show the superiority of the proposed method
over the state-of-the-art.

本文提出了一种基于最优传输的多源域自适应方法解决目标移位下的分类问题，实验结果表明相比现有技术在卫星图像分割任务上具有较好的性能。