# Super-Resolution of PROBA-V Images Using Convolutional Neural Networks

## TL;DR

This paper introduces a convolutional neural network for multi-image super-resolution of PROBA-V satellite images, effectively enhancing image quality despite challenges like cloud coverage and illumination changes.

## Contribution

It presents a novel CNN-based method for super-resolution that handles temporal and environmental variations in satellite imagery, improving image quality over previous techniques.

## Key findings

- Higher Peak Signal to Noise Ratio in reconstructed images
- Effective handling of illumination and cloud coverage variations
- Potential to enhance large historical earth observation datasets

## Abstract

ESA's PROBA-V Earth observation satellite enables us to monitor our planet at a large scale, studying the interaction between vegetation and climate and provides guidance for important decisions on our common global future. However, the interval at which high resolution images are recorded spans over several days, in contrast to the availability of lower resolution images which is often daily. We collect an extensive dataset of both, high and low resolution images taken by PROBA-V instruments during monthly periods to investigate Multi Image Super-resolution, a technique to merge several low resolution images to one image of higher quality. We propose a convolutional neural network that is able to cope with changes in illumination, cloud coverage and landscape features which are challenges introduced by the fact that the different images are taken over successive satellite passages over the same region. Given a bicubic upscaling of low resolution images taken under optimal conditions, we find the Peak Signal to Noise Ratio of the reconstructed image of the network to be higher for a large majority of different scenes. This shows that applied machine learning has the potential to enhance large amounts of previously collected earth observation data during multiple satellite passes.