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Online adaptation to distribution shifts in satellite image segmentationstands as a crucial yet underexplored problem. In this paper, we addresssource-free and online domain adaptation, i.e., test-time adaptation (TTA), forsatellite images, with the focus on mitigating distribution shifts caused byvarious forms of image degradation. Towards achieving this goal, we propose anovel TTA approach involving two effective strategies. First, we progressivelyestimate the global Batch Normalization (BN) statistics of the targetdistribution with incoming data stream. Leveraging these statistics duringinference has the ability to effectively reduce domain gap. Furthermore, weenhance prediction quality by refining the predicted masks using global classcenters. Both strategies employ dynamic momentum for fast and stableconvergence. Notably, our method is backpropagation-free and hence fast andlightweight, 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.

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