mlsanityData to 7 Oct 2026

On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution

arXiv 2302.11494 ↗First posted 22 Feb 2023cs.CV eess.IVcited by 3

Not on the map. Why?

Abstract (arXiv)

In this work, we study the problem of single-image super-resolution (SISR) of Sentinel-2 imagery. We show that thanks to its unique sensor specification, namely the inter-band shift and alias, that deep-learning methods are able to recover fine details. By training a model using a simple L1 loss, results are free of hallucinated details. For this study, we build a dataset of pairs of images Sentinel-2/PlanetScope to train and evaluate our super-resolution (SR) model.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 22 Feb 2023.

Builds on

6 of 16 references.

References · 6

  1. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  2. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  3. 2017The Perception-Distortion Tradeoffcited by 582
  4. 2018The relativistic discriminator: a key element missing from standard GANcited by 234
  5. 2018Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural networkcited by 44
  6. 2019Assessing the Sharpness of Satellite Images: Study of the PlanetScope Constellationcited by 2

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2023High-Resolution Building and Road Detection from Sentinel-2cited by 15
  2. 2024Cross-sensor super-resolution of irregularly sampled Sentinel-2 time seriescited by 6
  3. 2023L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagerycited by 3