SEN2NAIP is a large-scale dataset designed to support super-resolution in remote sensing by pairing low-resolution Sentinel-2 images with high-resolution NAIP imagery. It includes 2,851 original LR-HR pairs and over 35,000 synthetic pairs generated via a custom degradation model, enabling the development of models to enhance Sentinel-2 spatial resolution.
In recent years, increased attention has been given to image super-resolution (SR) techniques in remote sensing, which are aimed at reconstructing high-resolution imagery from low-resolution sources. To address ongoing challenges in evaluation, OpenSR-test has been presented as a comprehensive benchmark specifically designed for assessing SR in remote sensing, featuring tailored quality metrics and curated cross-sensor datasets.
A computationally efficient latent diffusion model is proposed for super-resolving Sentinel-2 imagery from 10 m to 2.5 m, with both visible and NIR bands incorporated and conditioned on the input to preserve spectral fidelity. Unlike previous approaches, pixel-level uncertainty maps are generated, allowing the reliability of the enhanced imagery to be assessed for critical remote sensing tasks.
A new framework, SEN2SR, was proposed to super-resolve Sentinel-2 images while preserving spectral and spatial consistency, using harmonized synthetic training data and a low-frequency constraint to minimize artifacts. Superior performance in resolution enhancement and downstream tasks was achieved and evaluated across various deep learning architectures with the aid of Explainable AI techniques.
Coming soon…
