Cross-sensor super-resolution of irregularly sampled Sentinel-2 time series
<|reference_start|>Cross-sensor super-resolution of irregularly sampled Sentinel-2 time series: Satellite imaging generally presents a trade-off between the frequency of acquisitions and the spatial resolution of the images. Super-resolution is often advanced as a way to get the best of both worlds. In this work, we investigate multi-image super-resolution of satellite image time series, i.e. how multiple images of the same area acquired at different dates can help reconstruct a higher resolution observation. In particular, we extend state-of-the-art deep single and multi-image super-resolution algorithms, such as SRDiff and HighRes-net, to deal with irregularly sampled Sentinel-2 time series. We introduce BreizhSR, a new dataset for 4x super-resolution of Sentinel-2 time series using very high-resolution SPOT-6 imagery of Brittany, a French region. We show that using multiple images significantly improves super-resolution performance, and that a well-designed temporal positional encoding allows us to perform super-resolution for different times of the series. In addition, we observe a trade-off between spectral fidelity and perceptual quality of the reconstructed HR images, questioning future directions for super-resolution of Earth Observation data.<|reference_end|>
@article{okabayashi2024cross-sensor,
    title={Cross-sensor super-resolution of irregularly sampled Sentinel-2 time
  series},
    author={Aimi Okabayashi (IRISA, OBELIX), Nicolas Audebert (CEDRIC - VERTIGO,
  CNAM, LaSTIG, IGN), Simon Donike (IPL), Charlotte Pelletier (OBELIX, IRISA)},
    journal={EARTHVISION 2024 IEEE/CVF CVPR Workshop. Large Scale Computer
  Vision for Remote Sensing Imagery, Jun 2024, Seattle, United States},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16409},
    primaryClass={cs.CV eess.IV}
}