# Remote Sensing Image Super-Resolution

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.

Remote sensing super-resolution previously injected low-resolution observations as dense spatial conditions into a diffusion Transformer, so small foreground objects were easily diluted by dominant backgrounds. MOSAIC organizes degraded observations into compact object-level and layout-level structural slots, fuses them across resolutions, and retrieves recurring structural priors from a shared prototype memory, then injects them into a frozen DiT4SR backbone through lightweight residual modulation. On UCMerced, AID and RSSCN7, PSNR is best at both scales, exceeding DiT4SR by 2.03 dB on UCMerced, 4.54 dB on AID and 1.49 dB on RSSCN7, with the lowest LPIPS on RSSCN7 and AID.

Remote sensing super-resolution previously injected low-resolution observations as dense spatial conditions into a diffusion Transformer, so small foreground objects were easily diluted by dominant backgrounds. MOSAIC organizes degraded observations into compact object-level and layout-level structural slots, fuses them across resolutions, and retrieves recurring structural priors from a shared prototype memory, then injects them into a frozen DiT4SR backbone through lightweight residual modulation. On UCMerced, AID and RSSCN7, PSNR is best at both scales, exceeding DiT4SR by 2.03 dB on UCMerced, 4.54 dB on AID and 1.49 dB on RSSCN7, with the lowest LPIPS on RSSCN7 and AID.