L1BSR: Exploiting Detector Overlap for Self-Supervised
Single-Image Super-Resolution of Sentinel-2 L1B Imagery

Best student paper
Ngoc Long Nguyen
Jérémy Anger
Gabriele Facciolo

L1BSR produces a 5m high-resolution (HR) output with all bands correctly registered from a single 10m low-resolution (LR) Sentinel-2 L1B image with misaligned bands. Note that our method is trained on real data with self-supervision, i.e. without any ground truth HR targets.
Project developed at the ENS Paris-Saclay, Centre Borelli and accepted at EarthVision 2023.


We propose L1BSR, a deep learning-based method for super-resolution and band alignment of Sentinel-2 L1B RGBN bands. It uses self-supervision on real L1B data to avoid the need for high-resolution ground truth. A cross-spectral registration network is trained to compute an optical flow between different spectral bands. Results show comparable performance to supervised methods on synthetic and real L1B data.

Proposed framework

Super-Resolution framework. Overview of our proposed self-supervised L1BSR framework for Sentinel-2 L1B at training time. Note that at inference time, only one input and the reconstruction module are required.
Cross-Spectral Registration framework. Training setup of our proposed cross-spectral registration (CSR) module.

 [Try our code] [Try our demo]

Sentinel-2 sensor layout and L1BSR dataset

Sensor layout of the Sentinel-2 MSI. The Sentinel-2 MSI carries 12 CMOS detectors for the VNIR bands, with adjacent detectors having overlapping fields of view that result in overlapping regions in level-1B (L1B) images. The push-broom acquisition is done in the vertical direction.
The L1BSR dataset includes 3740 pairs of overlapping image crops extracted from two L1B products. Each crop has a height of around 400 pixels and a variable width that depends on the overlap width between detectors for RGBN bands, typically around 120-200 pixels. In addition to detector parallax, there is also cross-band parallax for each detector, resulting in shifts between bands. Pre-registration is performed for both cross-band and cross-detector parallax, with a precision of up to a few pixels (typically less than 10 pixels).
Examples of overlapping L1B crops from the L1BSR dataset.

 [Download link. L1BSR dataset]

Qualitative comparison with a supervised SR method

We compare our self-supervised method L1BSR with a L1C-based supervised method, which uses PlanetScope images as ground-truth HR targets to train the SR model. The L1B and L1C images are from the same acquisition.


N. L. Nguyen, J. Anger, A. Davy, P. Arias, and G. Facciolo
L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery.
In EarthVision 2023.
(hosted on ArXiv)



This template was originally made by Phillip Isola and Richard Zhang for a colorful ECCV project; the code can be found here.