# Advances on CNN-based super-resolution of Sentinel-2 images

## TL;DR

This paper presents an improved CNN-based method for super-resolving Sentinel-2 satellite images, enhancing the spatial resolution of 20-meter bands by leveraging higher-resolution 10-meter bands, thus addressing resolution limitations.

## Contribution

An enhanced CNN approach that utilizes multi-resolution Sentinel-2 data to improve super-resolution of 20-meter bands, building on previous CNN methods.

## Key findings

- Achieved better spatial detail in super-resolved images.
- Demonstrated improved resolution over previous methods.
- Leveraged 10-meter bands to enhance 20-meter band resolution.

## Abstract

Thanks to their temporal-spatial coverage and free access, Sentinel-2 images are very interesting for the community. However, a relatively coarse spatial resolution, compared to that of state-of-the-art commercial products, motivates the study of super-resolution techniques to mitigate such a limitation. Specifically, thirtheen bands are sensed simultaneously but at different spatial resolutions: 10, 20, and 60 meters depending on the spectral location. Here, building upon our previous convolutional neural network (CNN) based method, we propose an improved CNN solution to super-resolve the 20-m resolution bands benefiting spatial details conveyed by the accompanying 10-m spectral bands.