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.

翻译：卫星图像分割中针对分布漂移的在线自适应是一个至关重要但尚未充分探索的问题。本文针对卫星图像的无源在线域自适应（即测试时自适应，TTA）问题展开研究，重点关注由多种图像退化形式引起的分布漂移缓解。为此，我们提出了一种包含两种有效策略的新型TTA方法。首先，利用输入数据流逐步估计目标分布的全局批归一化（BN）统计量，在推理过程中利用这些统计量可有效缩小域差距。其次，通过利用全局类别中心优化预测掩码来提升预测质量。两种策略均采用动态动量以实现快速稳定的收敛。值得注意的是，我们的方法无需反向传播，因此具有快速轻量的特点，非常适合对新域进行实时自适应。通过在多种域自适应场景下的全面实验，我们证明了该方法具有鲁棒性能。