Source-Free Online Domain Adaptive Semantic Segmentation of Satellite
  Images under Image Degradation
Fahim Faisal Niloy Kishor Kumar Bhaumik Simon S. Woo
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.