How Many Independent Samples Does a Satellite Image Contain? Generalization Bounds for Spatially Dependent Data

Title:How Many Independent Samples Does a Satellite Image Contain? Generalization Bounds for Spatially Dependent Data

Abstract:Machine learning classifiers for remote sensing imagery are typically evaluated as though every pixel were an independent sample. Spatial autocorrelation violates this assumption, since neighboring pixels carry redundant information which inflates sample sizes. How many independent samples does a satellite image actually contain? For an $n \times n$ image whose spatial correlation persists over a range of $r$ pixels, the effective sample size is $\Theta(n^2/r^2)$, not $n^2$. We prove this as a finite-sample upper bound for classifiers on spatially correlated data, and show via a matching lower bound that the rate is tight, and no algorithm can do better. We extend the results to images with directional correlation and spatially varying correlation structure. Our result justifies spatial cross-validation since block holdout with separation proportional to the correlation range achieves optimal generalization guarantees, while random holdout can underestimate confidence interval widths by a factor proportional to $r$. We validate the theory on synthetic data and satellite image tiles from three sensors (Landsat 8, Sentinel-2, and Sentinel-1).

Machine Learning (stat.ML); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2610.08227 [stat.ML]

(or arXiv:2610.08227v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.08227 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

IEEE Transactions on Geoscience and Remote Sensing (2026) vol. 64

https://doi.org/10.1109/TGRS.2026.3739158 Focus to learn more DOI(s) linking to related resources

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