Tackling the Satellite Downlink Bottleneck with Federated Onboard Learning of Image Compression

Abstract

Satellite data transmission is a crucial bottleneck for Earth observation applications. To overcome this problem, we propose a novel solution that trains a neural network on board multiple satellites to compress raw data and only send down heavily compressed previews of the images while retaining the possibility of sending down selected losslessly compressed data. The neural network learns to encode and decode the data in an unsupervised fashion using distributed machine learning. By simulating and optimizing the learning process under realistic constraints such as thermal, power and communication limitations, we demonstrate the feasibility and effectiveness of our approach. For this, we model a constellation of three satellites in a Sun-synchronous orbit. We use real raw, multispectral data from Sentinel-2 and demonstrate the feasibility on space-proven hardware for the training. Our compression method outperforms JPEG compression on different image metrics, achieving better compression ratios and image quality. We report key performance indicators of our method, such as image quality, compression ratio and benchmark training time on a Unibap iX10-100 processor. Our method has the potential to significantly increase the amount of satellite data collected that would typically be discarded (e.g., over oceans) and can potentially be extended to other applications even outside Earth observation. All code and data of the method are available online to enable rapid application of this approach.