Flow matching achieves single-step pixel accuracy and 20-step perceptual quality for Sentinel-2 super-resolution, outperforming diffusion and Real-ESRGAN while enabling large-scale 2.5 m land-cover products.
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3 Pith papers cite this work. Polarity classification is still indexing.
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SatFusion unifies multi-frame super-resolution and pansharpening by extracting semantic features from multiple LR multispectral frames and adding structural details from an HR panchromatic image, with an advanced panchromatic-guided variant.
citing papers explorer
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Flow matching for Sentinel-2 super-resolution: implementation, application, and implications
Flow matching achieves single-step pixel accuracy and 20-step perceptual quality for Sentinel-2 super-resolution, outperforming diffusion and Real-ESRGAN while enabling large-scale 2.5 m land-cover products.
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SatFusion: A Unified Framework for Enhancing Remote Sensing Images via Multi-Frame and Multi-Source Images Fusion
SatFusion unifies multi-frame super-resolution and pansharpening by extracting semantic features from multiple LR multispectral frames and adding structural details from an HR panchromatic image, with an advanced panchromatic-guided variant.
- Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration