Yan Zhang

Arbitrary-scale super-resolution for remote sensing images has mostly been conditioned on the relative upscaling factor between low- and high-resolution images, leaving out absolute spatial resolution in meters per pixel, a physical prior under which the same factor can correspond to very different texture distributions. SpatialSR instead conditions reconstruction on a target spatial resolution: it estimates the low-resolution image's absolute resolution to derive the required factor, draws resolution-specific texture style from a pretrained resolution-to-image diffusion model, and folds the diffusion process into a single-step training constraint. On Vaihingen at a 0.1 m/pixel target from 0.5 m/pixel sources, its segmentation mIoU is 66.7, above the listed comparison methods.

Arbitrary-scale super-resolution for remote sensing images has mostly been conditioned on the relative upscaling factor between low- and high-resolution images, leaving out absolute spatial resolution in meters per pixel, a physical prior under which the same factor can correspond to very different texture distributions. SpatialSR instead conditions reconstruction on a target spatial resolution: it estimates the low-resolution image's absolute resolution to derive the required factor, draws resolution-specific texture style from a pretrained resolution-to-image diffusion model, and folds the diffusion process into a single-step training constraint. On Vaihingen at a 0.1 m/pixel target from 0.5 m/pixel sources, its segmentation mIoU is 66.7, above the listed comparison methods.