HSRest: Benchmarking Hyperspectral Satellite Image Restoration Across Tasks, Data Regimes, and Regions

Hyperspectral satellite images provide valuable observations of the Earth, but their restoration is difficult to evaluate consistently because existing studies differ in data selection, degradation protocols, training regimes, and evaluation metrics. We introduce HSRest, a benchmark for systematic evaluation of hyperspectral image (HSI) restoration, comprising 23,228 non-overlapping 128 × 128 patches extracted from 946 EO-1 Hyperion scenes across 26 countries and six continents. HSRest covers three representative restoration tasks, namely Gaussian denoising, spatial super-resolution, and structured inpainting, with multiple degradation levels, and organizes the same corpus under three complementary evaluation settings. Setting I provides country- and scene-disjoint train/validation/test partitions for standard evaluation. Setting II defines fixed 10%, 20%, and 30% training subsets for low- resource evaluation. Setting III partitions all scenes into three geographically distinct groups for evaluation under geographic distribution shifts. We benchmark recent restoration methods using complementary spatial, spectral, and efficiency measures. Across tasks, training budgets, metrics, and held-out regions, method rankings change substantially, showing that conclusions drawn from a single split, degradation level, or reconstruction metric may not reliably characterize restoration performance. HSRest releases fixed partitions, degradation protocols, metadata, evaluation code, and baseline configurations, providing a reproducible testbed for data-efficient and region-robust HSI restoration.