AI RESEARCH
Whittaker-Henderson smoother for long satellite image time series interpolation
arXiv CS.AI
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ArXi:2604.00048v1 Announce Type: cross Whittaker smoother is a widely adopted solution to pre-process satellite image time series. Yet, two key limitations remain: the smoothing parameter must be tuned individually for each pixel, and the standard formulation assumes homoscedastic noise, imposing uniform smoothing across the temporal dimension. This paper addresses both limitations by casting the Whittaker smoother as a differentiable neural layer, in which the smoothing parameter is inferred by a neural network.